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## Über dieses Buch

This book highlights state-of-the-art developments in metaheuristics research. It examines all aspects of metaheuristic research including new algorithmic developments, applications, new research challenges, theoretical developments, implementation issues, in-depth experimental studies. The book is divided into two sections. Part I is focused on new optimization and modeling techniques based on metaheuristics. The chapters in this section cover topics from multi-objective problems with fuzzy data with triangular-valued objective functions, to hyper-heuristics optimization methodology, designing genetic algorithms, and also the cuckoo search algorithm. The techniques described help to enhance the usability and increase the potential of metaheuristic algorithms. Part II showcases advanced metaheuristic approaches to solve real-life applications issues. This includes an examination of scheduling, the vehicle routing problem, multimedia sensor network, supplier selection, bin packing, objects tracking, and radio frequency identification. In the fields covered in the chapters are of high-impact applications of metaheuristics. The chapters offer innovative applications of metaheuristics that have a potential of widening research frontiers. Altogether, this book offers a comprehensive look at how researchers are currently using metaheuristics in different domains of design and application.

## Inhaltsverzeichnis

### Chapter 1. Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization

Particle swarm optimization (PSO) is a stochastic algorithm based population that integrates social interactions of animals in nature. Adaptive Particle swarm optimization (APSO) as an amelioration of the original one, improve the performance of global search and gives better efficiency. The APSO defines four evolutionary states: exploration, exploitation, convergence, and jumping out. According to the state, the inertia weight and acceleration coefficients are controlled. In this paper, we integrate Hidden Markov Model Particle swarm optimization (HMM) in APSO to have a stochastic state classification at each iteration. Furthermore, to tackle the problem of the dynamic environment during iterations, an additional online learning for HMM parameters is integrated into the algorithm using online Expectation-Maximization algorithm. We performed evaluations on ten benchmark functions to test the HMM integration inside APSO. Experimental results show that our proposed scheme outperforms other PSO variants in major cases regarding solution accuracy and specially convergence speed.
Oussama Aoun, Malek Sarhani, Abdellatif El Afia

### Chapter 2. Possibilistic Framework for Multi-Objective Optimization Under Uncertainty

Optimization under uncertainty is an important line of research having today many successful real applications in different areas. Despite its importance, few works on multi-objective optimization under uncertainty exist today. In our study, we address combinatorial multi-objective problem under uncertainty using the possibilistic framework. To this end, we firstly propose new Pareto relations for ranking the generated uncertain solutions in both mono-objective and multi-objective cases. Secondly, we suggest an extension of two well-known Pareto-base evolutionary algorithms namely, SPEA2 and NSGAII. Finally, the extended algorithms are applied to solve a multi-objective Vehicle Routing Problem (VRP) with uncertain demands.
Oumayma Bahri, Nahla Ben Amor, El-Ghazali Talbi

### Chapter 3. Combining Neighborhoods into Local Search Strategies

This paper presents a declarative framework for defining local search procedures. It proceeds by combining neighborhoods by means of so-called combinators that specify when neighborhoods should be explored, and introduce other aspects of the search procedures such as stop criteria, solution management, and various metaheuristics. Our approach introduces these higher-level concepts natively in local search frameworks in contrast with the current practice which still often relies on their ad-hoc implementation in imperative language. Our goal is to ease the development, understanding, experimentation, communication and maintenance of search procedures. This will also lead to better search procedures where lots of efficiency gains can be made both for optimality and speed. We provide a comprehensive overview of our framework along with a number of examples illustrating typical usage pattern and the ease of use of our framework. Our combinators are available in the search component of the OscaR.cbls solver.
Renaud De Landtsheer, Yoann Guyot, Gustavo Ospina, Christophe Ponsard

### Chapter 4. All-Terrain Tabu Search Approaches for Production Management Problems

A metaheuristic is a refined solution method able to find a satisfying solution to a difficult problem in a reasonable amount of time. A local search metaheuristic works on a single solution and tries to improve it iteratively. Tabu search is one of the most famous local search, where at each iteration, a neighbor solution is generated from the current solution by performing a specific modification (called a move) on the latter. The goal of this chapter is to present tabu search approaches with enhanced exploration and exploitation mechanisms. For this purpose, the following ingredients are discussed: different neighborhood structures (i.e., different types of moves), guided restarts based on a distance function, and deconstruction/reconstruction techniques. The resulting all-terrain tabu search approaches are illustrated for various production problems: car sequencing, job scheduling, resource allocation, and inventory management.
Nicolas Zufferey, Jean Respen, Simon Thevenin

### Chapter 5. A Re-characterization of Hyper-Heuristics

Hyper-heuristics are an optimization methodology which ‘search the space of heuristics’ rather than directly searching the space of the underlying candidate-solution representation. Hyper-heuristic search has traditionally been divided into two layers: a lower problem-domain layer (where domain-specific heuristics are applied) and an upper hyper-heuristic layer, where heuristics are selected or generated. The interface between the two layers is commonly termed the “domain barrier”. Historically this interface has been defined to be highly restrictive, in the belief that this is required for generality. We argue that this prevailing conception of domain barrier is so limiting as to defeat the original motivation for hyper-heuristics. We show how it is possible to make use of domain knowledge without loss of generality and describe generalized hyper-heuristics which can incorporate arbitrary domain knowledge.
Jerry Swan, Patrick De Causmaecker, Simon Martin, Ender Özcan

### Chapter 6. POSL: A Parallel-Oriented Metaheuristic-Based Solver Language

For a couple of years, all processors in modern machines are multi-core. Massively parallel architectures , so far reserved for super-computers, become now available to a broad public through hardware like the Xeon Phi or GPU cards. This architecture strategy has been commonly adopted by processor manufacturers, allowing them to stick with Moore’s law. However, this new architecture implies new ways to design and implement algorithms to exploit its full potential. This is in particular true for constraint-based solvers dealing with combinatorial optimization problems. Here we propose a Parallel-Oriented Solver Language (POSL, pronounced “puzzle”), a new framework to build interconnected meta-heuristic based solvers working in parallel. The novelty of this approach lies in looking at solver as a set of components with specific goals, written in a parallel-oriented language based on operators. A major feature in POSL is the possibility to share not only information, but also behaviors, allowing solver modifications during runtime. Our framework has been designed to easily build constraint-based solvers and reduce the developing effort in the context of parallel architecture. POSL’s main advantage is to allow solver designers to quickly test different heuristics and parallel communication strategies to solve combinatorial optimization problems, usually time-consuming and very complex technically, requiring a lot of engineering.
Alejandro REYES-Amaro, Eric Monfroy, Florian Richoux

### Chapter 7. An Extended Neighborhood Vision for Hill-Climbing Move Strategy Design

Many combinatorial optimization problem solvers are based on stochastic local search algorithms, which mainly differ by their move selection strategies, also called pivoting rules. In this chapter, we aim at determining pivoting rules that allow hill-climbing to reach good local optima. We propose here to use additional information provided by an extended neighborhood for an accurate selection of neighbors. In particular, we introduce the maximum expansion pivoting rule which consists in selecting a solution which maximizes the improvement possibilities at the next step. Empirical experiments on permutation-based problem instances indicate that the expansion score is a relevant criterion to attain good local optima.
Sara Tari, Matthieu Basseur, Adrien Goëffon

### Chapter 8. Theory Driven Design of Efficient Genetic Algorithms for a Classical Graph Problem

This paper presents a principled way of designing a genetic algorithm which can guarantee a rigorously proven upper bound on its optimization time. The shortest path problem is selected to demonstrate how level-based analysis, a general purpose analytical tool, can be used as a design guide. We show that level-based analysis can also ease the experimental burden of finding appropriate parameter settings. Apart from providing an example of theory-driven algorithmic design, we also provide the first runtime analysis of a non-elitist population-based evolutionary algorithm for both the single-source and all-pairs shortest path problems.
Dogan Corus, Per Kristian Lehre

### Chapter 9. On the Impact of Representation and Algorithm Selection for Optimisation in Process Design: Motivating a Meta-Heuristic Framework

In an ideal world, it would be straightforward to identify the most suitable optimisation method to use in the solution of a given optimisation problem. However, although some methods may be more widely applicable than others, it is impossible a priori to know which method will work best. This may be due to the particular mathematical properties of the mathematical model, i.e. the formulation. It may also be due to the representation of the variables in the model. This combination of choices of method, representation and formulation makes it difficult to predict which combination may be best.
This paper presents an example from process engineering, the design of heat exchanger networks, for which two different representations for the same formulation are available. Two different heuristic optimisation procedures are considered. The results demonstrate that any given combination will not lead to the best outcome across a range of case studies. This motivates the need for a multi-algorithm, multi-representation approach to optimisation, at least for process design.
Eric S. Fraga, Abdellah Salhi, El-Ghazali Talbi

### Chapter 10. Manufacturing Cell Formation Problem Using Hybrid Cuckoo Search Algorithm

Cellular manufacturing, as one of the most important applications of Group Technology, has gained popularity in both academic research and industrial applications. The cell formation problem is considered the first and the foremost issue faced in the designing of cellular manufacturing systems that attempts to minimize the inter-cell movement of the products while maximize the machines utilization. This paper presents an adapted optimization algorithm entitled the cuckoo search algorithm for solving this kind of problems. The proposed method is tested on different benchmark problems; the obtained results are then compared to others available in the literature. The comparison result reveals that on 31 out of 35 problems (88.57%) the results of the introduced method are among the best results.
Bouchra Karoum, Bouazza Elbenani, Noussaima El Khattabi, Abdelhakim A. El Imrani

### Chapter 11. Hybridization of Branch and Bound Algorithm with Metaheuristics for Designing Reliable Wireless Multimedia Sensor Network

Reliability is a key topic for Wireless Multimedia Sensor Networks (WMSNs) design which involves connectivity and coverage issues with node placement. The main contribution of this chapter is to deploy sensor nodes to maximize the WMSN reliability under a given budget constraint by considering terrain and device specifications. The reliable WMSN design with deployment, connectivity and coverage has NP-hard complexity, therefore a new hybridization of an exact algorithm with metaheuristics is proposed. A Branch&Bound (B&B) approach is embedded into Hybrid Simulated Annealing (HSA) and Hybrid Genetic Algorithm (HGA) to orient the cameras exactly. Since the complexity of the network reliability problem is NP-complete, a Monte Carlo (MC) simulation is used to estimate the network reliability . Experimental study is done on synthetically generated terrains with different scenarios. The results show that HGA outperforms the other approaches especially in large-sized sets.
Omer Ozkan, Murat Ermis, Ilker Bekmezci

### Chapter 12. A Hybrid MCDM Approach for Supplier Selection with a Case Study

The supplier selection problem is one of the strategic decisions that have a significant impact on the performance of the supply chain . In this study, supplier selection problem of a well-known refining company in Africa is investigated and an integrated DEMATEL-ANP-TOPSIS methodology is used to select the best supplier providing the most customer satisfaction for the criteria determined. DEMATEL method is used in order to detect the cause and effect interaction among main criteria. The weights of criteria are calculated using Analytic Network Process approach and then the modified TOPSIS has been applied for the final selection. The supplier which is closest to the ideal solution and farthest from the negative ideal solution is selected as the best supplier.
Hanane Assellaou, Brahim Ouhbi, Bouchra Frikh

### Chapter 13. A Multi-Objective Optimization via Simulation Framework for Restructuring Traffic Networks Subject to Increases in Population

Traffic network design is a complex problem due to its nonlinear and stochastic nature. The Origin-Destiny Traffic Assignment Problem is particular case of this problem. In it, we are faced with an increase in the system vehicle population; and, we want to determine where to set the generating nodes and the traffic consumers, minimizing the current system and trying to reduce necessary investment. Performing optimizations in an analytical way in this kind of problems tends to be really complicated and a bit impractical, since it is difficult to estimate vehicle flows. In this chapter, we propose the use of a Multi-Objective Particle Swamp Optimization together with Traffic Simulations in order to generate restructuring alternatives that optimize both, traffic flow and cost associated to this restructure. This approach allows to obtain a very good approximation of the Pareto Frontier of the problem, with a fast convergence to the low infrastructure cost solutions and a total coverage of the frontier when the number of iterations is high.
Enrique Gabriel Baquela, Ana Carolina Olivera

### Chapter 14. Hybrid Metaheuristic for Air Traffic Management with Uncertainty

To sustain the rapidly increasing air traffic demand, the future air traffic management system will rely on a concept, called Trajectory-Based Operations (TBO), that will require aircraft to follow an assigned 4D trajectory (time-constrained trajectory) with high precision. TBO involves separating aircraft via strategic (long-term) trajectory deconfliction rather than the currently-practicing tactical (short-term) conflict resolution. In this context, this chapter presents a strategic trajectory planning approach aiming at minimizing the number of conflicts between aircraft trajectories for a given day. The proposed methodology allocates an alternative departure time, a horizontal flight path, and a flight level to each aircraft at a nation-wide scale. In real-life situations, aircraft may arrive at a given position with some uncertainties on its curvilinear abscissa due to external events. To ensure robustness of the strategic trajectory plan, the aircraft arrival time to any given position will be represented here by a probabilistic distribution over its nominal assigned arrival time. The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the probability of potential conflicts between trajectories. A hybrid-metaheuristic optimization algorithm has been developed to solve this large-scale mixed-variable optimization problem. The algorithm is implemented and tested with real air traffic data taking into account uncertainty over the French airspace for which a conflict-free and robust 4D trajectory plan is produced
S. Chaimatanan, D. Delahaye, M. Mongeau

### Chapter 15. Sampling-Based Genetic Algorithms for the Bi-Objective Stochastic Covering Tour Problem

The paper investigates a sampling-based extension of the NSGA-II algorithm, applied to the solution of a bi-objective stochastic covering tour problem. The proposed extension uses variable samples for gradually improving approximations to the Pareto front. The approach is evaluated on a test benchmark for a humanitarian logistics application with data from Senegal. Comparisons to alternative solution techniques, in particular also to the exact solution of the deterministic counterpart problem based on a fixed sample, show the superiority of our approach.
Michaela Zehetner, Walter J. Gutjahr

### Chapter 16. A Metaheuristic Framework for Dynamic Network Flow Problems

Dynamic network problems is a very interesting topic in modeling real life situations where we aim to send some flow to a given destination within time dependent parameters. This can occur in many applications such in evacuation of people or vehicules in emergency time.
The majority of existing algorithms are based on mathematical approximations. However, this work proposes another technique based on metaheuristics. A general framework is provided in both single and population based algorithms. Therefore, basic search techniques are proposed such as the crossover or the mutation. Moreover, solution representations are given within a general metaheuristical scheme. In addition, we assess a genetic algorithm by an experimental study is conducted on a case study of a building evacuation.
M. Hajjem, H. Bouziri, El-Ghazali Talbi

### Chapter 17. A Greedy Randomized Adaptive Search for the Surveillance Patrol Vehicle Routing Problem

In this chapter a new rich vehicle routing problem is introduced, the Surveillance Patrol Vehicle Routing Problem (SPVRP) . This problem came out from a real need of a surveillance company to create fairer routing plans for its security patrols. The problem consist into routing a set of patrols in order to visit a set of checkpoints. Each checkpoint requires one or more visits, each one of which, to be performed within a fixed time window. A minimum time spacing between two consecutive visits should be observed. The goal is to minimize cost while minimizing, at the same time, time windows and minimum spacing constraints violations. In order to avoid repetitiveness in the routes and to provide more unpredictable routing plans, the company looks for a pool of sensibly different high quality solutions form which, each night they can choose the routing plan to be followed. To address this problem a Greedy Randomized Adaptive Search algorithm (GRASP) , is used to provide good solutions and a further GRASP algorithm is used to generate pools of good solutions. The quality of a pool is measured both in terms of averaged quality of the solutions in the pools and in terms of diversity among each others. Experimental tests on real instances are reported.
Simona Mancini

### Chapter 18. Strip Algorithms as an Efficient Way to Initialise Population-Based Metaheuristics

The Strip Algorithm (SA) is a constructive heuristic which has been tried on the Euclidean Travelling Salesman Problem (TSP) and other planar network problems with some success. Its attraction is its efficiency. In its simplest form, it can find tours of length $$\varOmega \ (\sqrt{n})$$ in O (n log n) operations where n is the number of nodes. Here, we set out to investigate new variants such as the 2-Part Strip Algorithm (2-PSA), the Spiral Strip Algorithm (SSA) and the Adaptive Strip Algorithm (ASA). The latter is particularly suited for Euclidean TSPs with non-uniform distribution of cities across the grid; i.e problems with clustered cities. These cases present an overall low density, but high localised densities. ASA takes this into account in that smaller strips are generated where the density is high. All three algorithms are analysed, implemented and computationally tested against each other and the Classical Strip Algorithm. Computational results are included.
Birsen İrem Selamoğlu, Abdellah Salhi, Muhammad Sulaiman

### Chapter 19. Matheuristics for the Temporal Bin Packing Problem

We study an extension of the Bin Packing Problem , where items consume the bin capacity during a time window only. The problem asks for finding the minimum number of bins to pack all the items respecting the bin capacity at any instant of time. Both a polynomial-size formulation and an extensive formulation are studied. Moreover, various heuristic algorithms are developed and compared, including greedy heuristics and a column generation based heuristic. We perform extensive computational experiments on benchmark instances to evaluate the quality of the computed solutions with respect to strong bounds based on the linear programming relaxation of the proposed formulations.
Fabio Furini, Xueying Shen

### Chapter 20. A Fast Reoptimization Approach for the Dynamic Technician Routing and Scheduling Problem

The Technician Routing and Scheduling Problem (TRSP) consists in routing staff to serve requests for service, taking into account time windows, skills, tools, and spare parts. Typical applications include maintenance operations and staff routing in telecoms, public utilities, and in the health care industry. In this paper we tackle the Dynamic TRSP (D-TRSP) in which new requests appear over time. We propose a fast reoptimization approach based on a parallel Adaptive Large Neighborhood Search (RpALNS) able to achieve state-of-the-art results on the Dynamic Vehicle Routing Problem with Time Windows. In addition, we solve a set of randomly generated D-TRSP instances and discuss the potential gains with respect to a heuristic modeling a human dispatcher solution.
V. Pillac, C. Guéret, A. L. Medaglia

### Chapter 21. Optimized Air Routes Connections for Real Hub Schedule Using SMPSO Algorithm

The choice to open new routes for air carriers, airports and regional governments have some tools to promote desirable connections to be offered toward specific destinations. With a given flight program, the air carrier decision to open new routes faces several constraints and affects the flight schedules in a remarkable way. This work is distinguished by the fact of being the first to introduce the problem of connectivity in the network of an airline whose main activity is based on air hub structure, optimizing the insertion of new airline routes while ensuring the best fill rate seats and avoiding significant delays during correspondence. Quality of Service Index (QSI) will be considered as a duel parameter for the profit of a new opened market. This aspect of decision making is formulated as multi-objective problem by testing the impact of a new insertion in term of delays, generated with related costs and financial gain, and the quality of service offered to a target customers. The SMPSO Algorithm is adopted to generate a Pareto-optimal front composed of many optimal departure times toward the new opening insuring the best filling ratio with minimum connecting times. Experiences are based on real instance of Royal Air Maroc flights schedule on the hub of Casablanca .
H. Rahil, B. Abou El Majd, M. Bouchoum

### Chapter 22. Solving the P/Prec, p j ,C ij /C max Using an Evolutionary Algorithm

In this chapter, we tackle the problem of scheduling a set of related tasks on a set of identical processors taking into account the communication delays with the objective of minimizing the maximal completion time . This problem is well known as NP-Hard. As Particle swarm optimization PSO is a promising approach for solving NP-complete problems due to its simple implementation, fast convergence and its few parameters to adjust, the main contribution of this research is to use for the first time PSO to solve the multiprocessor scheduling problem with communication delays . The proposed approach HEA-LS is a hybrid algorithm involving particle swarm optimization PSO and local search algorithm LS. Experiments conducted on several benchmarks known in the literature prove the effectiveness of our approach and show that it compares very well to the state of the art methods.
Dalila Tayachi

David Meignan

### Chapter 24. Surrogate-Assisted Multiobjective Evolutionary Algorithm for Fuzzy Job Shop Problems

We consider a job shop scheduling problem with uncertain processing times modelled as triangular fuzzy numbers and propose a multiobjective surrogate-assisted evolutionary algorithm to optimise not only the schedule’s fuzzy makespan but also the robustness of schedules with respect to different perturbations in the durations. The surrogate model is defined to avoid evaluating the robustness measure for some individuals and estimate it instead based on the robustness values of neighbouring individuals, where neighbour proximity is evaluated based on the similarity of fuzzy makespan values. The experimental results show that by using fitness estimation, it is possible to reach good fitness levels much faster than if all individuals are evaluated.
Juan José Palacios, Jorge Puente, Camino R. Vela, Inés González-Rodríguez, El-Ghazali Talbi

### Chapter 25. Towards a Novel Reidentification Method Using Metaheuristics

Tracking multiple moving objects in a video sequence can be formulated as a profile matching problem. Reidentifying a profile within a crowd is done by a matching process between the tracked person and the different moving individuals within the same frame. In that context, the feature matching task can be approximated to a search for the profile that maximizes a considered similarity measure. In this work, we introduce a novel Modified Cuckoo Search (MCS) based reidentification algorithm. A complex descriptor representing each moving person is built from different low level visual features such as the color and the texture components. We make use of a database that involves all previously detected descriptors, forming therefore a discrete search space where the sought solution is a descriptor and its quality is represented by its similarity to the query profile. The approach is evaluated within a multiple object tracking scenario, and a validation process using the normalized cross correlation method to accept or reject the obtained reidentification results is included. The experimental results show promising performances in terms of computation cost as well as reidentification rate.

### Chapter 26. Facing the Feature Selection Problem with a Binary PSO-GSA Approach

Feature selection has become the focus of much research in many areas where we can face the problem of big data or complex relationship among features. Metaheuristics have gained much attention in solving many practical problems, including feature selection. Our contribution in this paper is to propose a binary hybrid metaheuristic to minimize a fitness function representing a trade-off between the classification error of selecting the feature subset and the corresponding number of features. This algorithm combines particle swarm optimization (PSO) and gravitational search algorithm (GSA). Also, a mutation operator is integrated to enhance population diversity. Experimental results on ten benchmark dataset show that our proposed hybrid method for feature selection can achieve high performance when comparing with other metaheuristic algorithms and well-known feature selection approaches.
Malek Sarhani, Abdellatif El Afia, Rdouan Faizi

### Chapter 27. An Optimal Deployment of Readers for RFID Network Planning Using NSGA-II

Radio frequency identification (RFID) is an automated data collection technology with the aim to facilitate data acquisition and storage without human intervention. RFID process depends on radio-frequency waves to transfer data between a reader and an electronic tag attached to an item, in order to identify objects or persons, which allows an automated traceability. The deployment of RFID readers is an important component in RFID system, and plays a key role in RFID Network Planning (RNP). Therefore, in order to optimize the deployment of RFID reader problem, we propose a new approach based on multi-level strategy using as main objectives the coverage, the number of deployed readers and the interference. In this way, Non-dominated Sorting Genetic algorithm II (NSGA-II) is adopted in order to minimize the total quantity of readers required to identify all tags in a given area. The proposed multi-level approach based on NSGA-II algorithm has a several attractive features which makes it ideal for our research and the simulation results show its effectiveness and performance.

### Chapter 28. An Enhanced Bat Echolocation Approach for Security Audit Trails Analysis Using Manhattan Distance

Security Audit Trail Analysis problem consists in detecting predefined attack scenarios in the audit trails. Each attack scenario is defined by a number of occurrences of auditable events. This problem is classified as an NP-Hard combinatorial optimization problem. In this paper, we propose to use the Bat echolocation approach to solve such problem. The proposed approach named an Enhanced Binary Bat Algorithm (EBBA) is an improvement of Bat Algorithm (BA). The fitness function is defined as the global attacks risks. In order to improve , the fitness function is combined with the Manhattan distance measure. Thus, intrusion detection process is guided, on one hand, by the fitness function that aims to maximize the global attacks risks and, on the other hand, by the Manhattan distance that attempts to reduce false Positives and false negatives. The best solution retained has the smallest Manhattan distance. Experiments show that the use of the Manhattan distance improves substantially the intrusion detection quality. The comparative study proves the effectiveness of the proposed approach to make correct prediction.

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