Intelligent Information Processing XII

Table of Contents

1. Neural and Evolutionary Computing

   1. Frontmatter

   2. Empirical Evaluation of Evolutionary Algorithms with Power-Law Ranking Selection

      Duc-Cuong Dang, Anton V. Eremeev, Xiaoyu Qin

      Abstract

      It has been proven that non-elitist evolutionary algorithms (EAs) with proper selection mechanisms, including the recently proposed power-law ranking selection, can efficiently escape local optima on a broad class of problems called SparseLocalOpt \(_{\alpha ,\varepsilon }\), where elitist EAs fail. However, those theoretical upper bounds on the runtime are not tight as they require large populations and a tight balance between mutation rates and selection pressure to keep the algorithms operating near the so-called “error threshold”. This paper empirically clarifies the significance of these theoretical requirements and makes a series of performance comparisons between the non-elitist EA using power-law ranking selection and other EAs on various benchmark problems.

      Our experimental results show that non-elitist EAs optimise the Funnel problem with deceptive local optimum significantly faster with power-law ranking selection than with tournament selection. Furthermore, power-law selection outperforms UMDA and the (1+1) EA in our experiments on the NK-Landscape and Max k-Sat problems, but yields to the \((\mu ,\lambda )\)-selection, tournament selection, and the self-adaptive MOSA-EA. On the unicost set cover problems, the EA with power-law selection shows competitive results.

   3. An Indicator Based Evolutionary Algorithm for Multiparty Multiobjective Knapsack Problems

      Zhen Song, Wenjian Luo, Peilan Xu, Zipeng Ye, Kesheng Chen

      Abstract

      As a special case of the multiobjective optimization problem, the multiobjective knapsack problem (MOKP) widely exists in real-world applications. Currently, most algorithms used to solve MOKPs assume that these problems involve only one decision maker (DM). However, some complex MOKPs often involve more than one decision makers and we call such problems multiparty multiobjective knapsack problems (MPMOKPs). Existing algorithms cannot solve MPMOKPs effectively. To the best of our knowledge, there is only a little attention paid to MPMOKPs. In this paper, inspired by existing SMS-EMOA, we propose a novel indicator-based algorithm called SMS-MPEMOA to solve MPMOKPs, which aims to search solutions to satisfy all decision makers as much as possible. SMS-MPEMOA is compared with several state-of-the-art multiparty multiobjective optimization algorithms (MPMOEAs) on the benchmarks and the experimental results demonstrate that SMS-MPEMOA is very competitive.

   4. Ensemble Strategy Based Hyper-heuristic Evolutionary Algorithm for Many-Objective Optimization

      Wang Qian, Zhang Jingbo, Cui Zhihua

      Abstract

      Many-objective Optimization problems (MaOPs), with four or more objectives are difficult to solve, is a kind of common optimization problems in actual industrial production. In recent years, a large number of many-objective optimization algorithms (MaOEAs) have been proposed to solve various types of MaOPs. However, in practical problems, it is usually hard to improve the existing optimization algorithms or make a lot of attempts for MaOEAs because the true Pareto surface is usually unknown in a new MaOPs, which is a time-consuming and uncertain task. In this paper, inspired by the selective hyper heuristic optimization algorithm, we propose an integrated hyper-heuristic many-objective optimization algorithm (MaOEA-EH), which can integrate the existing advanced MaOEAs by simulating the PBFT consensus mechanism in the blockchain, and select the best algorithm for the current problem through the voting-election method in the iterative process. Numerical results show that our algorithm performs well on various many-objective problems.

   5. Rolling Horizon Co-evolution for Snake AI Competition

      Hui Li, Jiayi Zhou, Qingquan Zhang

      Abstract

      The Snake game, a classic in the gaming world, gains new dimensions with the Snake AI competition, where two players controlled by AI algorithms can now compete simultaneously in the same game session. This competition holds significance in advancing our understanding of artificial intelligence (AI) algorithms. In the 2020 and 2021 Snake AI competitions, popular algorithms, using graph-based search or heuristic strategies, demonstrate competitive performance, such as the A* algorithm, Monte Carlo Tree Search (MCTS). Contrary to these heuristic approaches, the Rolling Horizon Co-evolution Algorithm (RHCA), characterised by its core principles of rolling horizon evaluation and co-evolution, maintains two populations, one for each player, to co-evolve with each other without reliance on heuristics. RHCA has been verified its effectiveness in a two-player spaceship game. In this paper, we extend the RHCA application to the two-player Snake AI game, comparing it with other state-of-the-art methods. Additionally, we introduce various obstacles to create different complex scenarios, ensuring a comprehensive analysis. Experimental results reveal RHCA’s superior and stable performance, especially in resource-constrained and complex scenarios. Furthermore, an analysis of RHCA’s behaviours across maps with diverse obstacle scenarios highlights its ability to make intelligent decisions in competing with state-of-the-art methods.

   6. Training Artificial Immune Networks as Standalone Generative Models for Realistic Data Synthesis

      Siphesihle Philezwini Sithungu, Elizabeth Marie Ehlers

      Abstract

      In recent years, generative modelling has become a significant area of computer science research and artificial intelligence. This has been primarily due to the fact that generative models are useful in addressing the class imbalance problem inherent in some datasets. By generating synthetic data samples for underrepresented classes with a decent amount of variation through random noise, classification models could be trained more efficiently. The popularity of generative models was also increased by the prospect of being able to generate previously non-existent samples of images, audio and video for other creative tasks not related to addressing the class imbalance in datasets. This paper presents exploratory research to train an artificial immune network as a standalone generative model (called a generative adversarial artificial immune network, or GAAINet) using purely immunological computation concepts, such as antibody affinity, clonal selection and hypermutation. Experimental results show that the resulting generator artificial immune network could generate human-recognisable synthetic handwritten digits without any prior knowledge of the MNIST handwritten digits dataset.

   7. Structure Optimization for Wide-Channel Plate Heat Exchanger Based on Interval Constraints

      Yinan Guo, Guoyu Chen, Dongzhang Jiang, Tong Ding, Wenbo Li

      Abstract

      Wide-channel plate heat exchanger is a widely-used high performance heat exchanger, and its structure has a significant effect on heat exchange effect. However, the density and flow rate of the heat transfer medium is uncertain, and we only can obtain their possible ranges. Based on this, interval number is introduced to describe uncertainty factor, and then formulate the interval constraint of wide-channel plate heat exchanger. The triangular fuzzy number is employed to define the degree of constraint violation. Due to the difficulty of modeling heat exchange efficiency, its surrogate model is trained by neural network. To solve this issue, multi-objective particle swarm optimization algorithm is developed to find the optimal structural variable of heat exchanger under uncertain conditions. The experimental results indicate that the proposed algorithm obtains the structure variable of heat exchanger with the most preferable heat effect and lowest cost quickly.

   8. Genetic Algorithm Driven by Translational Mutation Operator for the Scheduling Optimization in the Steelmaking-Continuous Casting Production

      Lin Guan, Yalin Wang, Xujie Tan, Chenliang Liu

      Abstract

      The scheduling optimization of industrial processes is crucial for enhancing production capacity and minimizing energy consumption. In the realm of continuous casting, the expansion of the scheduling scale and the increasing number of scheduling objects pose challenges for genetic algorithms in swiftly generating optimal solutions that adhere to constraints. Prolonged scheduling decision times and difficulties in ensuring constant pouring constraints are critical issues that require urgent resolution in the continuous casting scheduling problem within steelmaking. This paper proposes a genetic algorithm driven by translational mutation operator for the scheduling optimization in the steelmaking-continuous casting production named TMGA. Incorporating continuous pouring information in the encoding process guarantees uninterrupted pouring during the casting stage. Furthermore, applying the translational mutation operator is instrumental in elevating the search efficiency for the global optimal solution, consequently diminishing scheduling decision times. To validate the effectiveness of the proposed approach, this study conducts a rigorous examination involving a numerical simulation case and two ablation experiments. The experimental results demonstrate the superior performance of TMGA compared to other methods.

   9. Adaptive Genetic Algorithm with Optimized Operators for Scheduling in Computer Systems

      Yu. V. Zakharova, M. Yu. Sakhno

      Abstract

      Modern computing and networking environments provide the important problems of efficient using such resources as energy and cores or processors. It is based on the possibility of dynamically varying the speed of processors and using parallel calculations in the execution of operations. We consider the NP-hard speed scaling scheduling problem with energy constraints and parallelizable jobs. Each job must be executed on the given number of processors. Processors can vary their speeds dynamically. It is required to assign speeds to jobs and schedule them such that the total completion time is minimized under the given energy budget. An adaptive genetic algorithm with optimized crossover operators is proposed. The optimal recombination problem is solved in the crossover operator. This problem is aimed at searching for the best possible offspring following the well-known gene transmitting property. The experimental evaluation shows that the algorithm outperforms the known metaheuristics and demonstrates the perspectives of using adaptive techniques and optimized operators.

   10. A Data-Driven Framework for Whole-Brain Network Modeling with Simultaneous EEG-SEEG Data

       Kexin Lou, Jingzhe Li, Markus Barth, Quanying Liu

       Abstract

       Whole-brain network modeling (WBM) offers a pivotal tool to explore the large-scale spatiotemporal dynamics of the brain at rest, during cognitive tasks, and under external stimulation. However, it is unclear how to fuse multi-modal neural dynamics in a united WBM framework and predict the whole-brain spatiotemporal neural responses to electrical stimulation. In this study, we present a computational framework with whole-brain network modeling, parameter optimization, and model validation using simultaneous EEG-SEEG data during intracranial brain stimulation. To test the efficacy of WBM in revealing brain-wide neural dynamics, our experiments utilize synthetic electrophysiological data, real EEG data, and real EEG-SEEG signals. Experimental results demonstrate that our WBM framework accurately captures the spatiotemporal brain activities by jointly leveraging the higher spatial resolution from SEEG and the whole-brain coverage from EEG. Notably, our model shows a higher correlation between the functional connectivity (FC) matrix of EEG and that of the inferred whole-brain neural dynamics from WBM (r=0.86), compared to the FC from EEG source localization (r=0.48). Together, we demonstrate the capability and flexibility of WBM framework to uncover the whole-brain spatiotemporal neural activity and its potential to provide new insights into the input-response mechanism of the brain.

2. Recommendation and Social Computing

   1. Frontmatter

   2. Secure and Negotiate Scheme for Vehicle-to-Vehicle Communications in an IoV

      Jinquan Hou, Yuqiu Jian, Guosheng Xu, Qiang Cao, Guoai Xu

      Abstract

      The exchange of real-time data between vehicle-to-vehicle communications is crucial in the Internet of Vehicles (IoV) for vehicle-intelligent decisions. However, malicious and false communication data may cause serious personal safety accidents. Confirming the authenticity of the identities of both parties and encrypting communication content before communication is the first line of defense to ensure system security. Therefore, to secure the vehicle-to-vehicle communications, this paper proposes a secure and efficient authentication and key agreement scheme with lightweight operation. Our scheme achieves vehicle-to-vehicle authentication and establishes a session key to encrypt subsequent communication content with only lightweight operations such as symmetric encryption algorithms and hash functions. Furthermore, our scheme provides many ideal attributes, such as forward secrecy, which ensures that the final compromised of the system will not affect the previous communication content. Besides, we prove the security of the proposed scheme through heuristic analysis and BAN logic analysis and analyze the performance of the proposed scheme via comparing the computational cost and communication cost with three state-of-the-art related schemes. The results show that the proposed scheme has high communication efficiency.

   3. Flexible k-anonymity Scheme Suitable for Different Scenarios in Social Networks

      Mingmeng Zhang, Yuanjing Hao, Pengao Lu, Liang Chang, Long Li

      Abstract

      Social networks not only help expand interpersonal interactions, enable data analysis, and implement intelligent recommendations, but also can deeply examine social structures and dynamic changes between individuals, making them an indispensable part of contemporary society. However, malicious entities pose a significant threat to user identity and relationship information within social networks, raising concerns about privacy and security issues. Although existing k-anonymity schemes provide certain privacy protection, they lack the flexibility to adjust the intensity of privacy protection according to specific scenarios and user preferences, thus seriously compromising the utility of anonymized data. Based on the isomorphic algorithm, this paper proposes a new structural anonymity algorithm called α-partial isomorphic anonymity (α-PIA) to meet the privacy protection and data usage requirements in different scenarios of social networks. By capturing graph structure features at different levels to calculate the similarity between nodes, α-PIA can improve clustering quality. Extensive experiments are carried out based on two public datasets. Experimental results show that compared with similar schemes, α-PIA achieves better results in terms of information loss, average clustering coefficient and average shortest path length and better balances the privacy protection and practicality of graph data.

   4. A Recommendation Algorithm Based on Automatic Meta-path Generation and Relationship Aggregation

      Yuying Wang, Jing Zhou, Yifan Ji, Qian Liu, Jiaying Wei

      Abstract

      Knowledge Graph (KG) contains rich semantic information and supports knowledge reasoning. In recent years, introducing KG as auxiliary information into the recommender system has become one common measure for improving recommendation quality. The unified graph, which is constructed from the KG and user-item matrix in recommender systems, contains meta-paths formed by single-hop/continuous multi-hop connectivity relationships, and these meta-paths can assist modeling of user preferences. The quality of manually designed meta-paths is prone to the type and number of human-defined meta-paths. Moreover, the process of defining meta-paths is time-consuming and labor-intensive, and inadequate sufficient considerations in design will have an adverse impact on the quality of recommendations. We propose a self-supervised meta-path generation approach that does not rely on domain knowledge to select valuable path information from the unified graph and can deliver high-quality recommendations and reduce noises. Previous studies on meta-paths mainly focused on the neighbor information of nodes and ignored the edges that represents relationships between nodes. We develop a meta-path-based relational path-aware strategy to discover the relational information included within the meta-path. To make the use of the global structure in the unified graph and the information within the local scope in the user-item bipartite graph and KG, a two-level relationship aggregator to fully aggregate the fine-grained semantic information and multi-hop semantic associations is also proposed. We conducted experiments on two public datasets, MovieLens and Book-Crossing to verify the effectiveness of the proposed algorithm. The experimental results show that the recommendation algorithm outperforms the baseline models in terms of AUC, Recall@K, and F1 in most cases.

   5. Cooperative Coevolution for Cross-City Itinerary Planning

      Ziyu Zhang, Peilan Xu, Zhaoguo Wang, Wenjian Luo

      Abstract

      The itinerary planning problem plays a pivotal role in the tourism industry, involving the selection of an optimal tour route from multiple preferred points of interest (POIs) chosen by travelers while considering their diverse needs. However, as tourism expands and transportation becomes more accessible, there is a growing preference among travelers for planning single trips across multiple cities-referred to as cross-city itinerary planning. This paper introduces a novel approach, called CCIP, the cooperative coevolution framework for cross-city itinerary planning, which employs a divide-and-conquer method to automatically devise scalable cross-city itineraries, accounting for travelers’ preferences regarding time and travel choices. Experimental evaluations on real datasets from various cities in Jiangsu Province demonstrate that the proposed algorithm outperforms two classical multi-objective optimization algorithms, as measured by the HV metric.