Hybrid Genetic Bees Algorithm (GBA) for Continuous and Combinatorial Optimisation Problems

The Bees Algorithm (BA) has proven its effectiveness in performing optimisation in many problems in recent years with many applications, but the global search of the BA is not its strength. This chapter summarised reports on a hybrid model called the Genetic Bees Algorithm (GBA) to enhance the BA for both continuous and combinatorial optimisation problems. For continuous problems, GBA in training a multilayer perceptron (MLP) is reported for the first time to solve real-world problems. Experimental results show that GBA provides significantly better performance than Particle Swarm Optimisation (PSO) in training MLP with higher accuracy. For combinatorial problems, in the two-stage flow shop scheduling problem (TFSSP), three methods, such as Particle Swam Optimiser (PSO), Genetic Algorithm (GA) and GBA, are compared. GBA is competitive and better than PSO and GA in a majority of instances in terms of results, proving that GBA is a realistic and efficient solution to the TFSSP. Finally, GBA proves its strength in solving the Just-in-Time Job Shop Scheduling Problem (JITJSSP) by solving a set of 36 benchmark instances ranging from 20 to 200 operations, the outcomes obtained from that are then compared to an exact method, two recent studies, and best-known solutions. The results show that the GBA has its strengths and weaknesses in solving the JITJSSP, and it performs well in some instances but does not perform well in others.