Multi-agent learning technique inspired from software engineering model for permutation coded GA

Abstract

Hybrid genetic algorithms (HGAs) have recognized significant attention in recent years and being progressively more used to solve real-world problems. HGA is the forms of assimilation between genetic algorithm and other search optimization techniques to improve the overall performance of the genetic algorithm (GA). GA can be hybridized with many other bio-inspired heuristic algorithms such as particle swarm optimization, cuckoo search genetic algorithm, firefly algorithm-genetic algorithm, real coded genetic algorithm-artificial fish swarm algorithm, etc., multi-agent system (MAS) is a new paradigm for conceptualizing, designing and optimizing the solution models. Multi-agent learning concept improves the outcome of the MAS and the type of learning process involved in it plays a vital role. Incremental model is a well-known software development process in which the system yields better effect at the end of every cycle. In this paper, software engineering inspired incremental model based multi-agent learning model for permutation coded genetic algorithms has been proposed. One of the famous combinatorial hard problems of traveling salesman problem (TSP) is being chosen as the test bed and the experiments are performed on large sized benchmark TSP instances obtained from standard TSPLIB. The experimental results support that the proposed model perform better than existing best working initialization methods in terms of convergence rate, error rate and computation time.