Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations

Two popular evolutionary techniques used for solving multi-objective optimization problems, namely, genetic algorithm and simulated annealing, are discussed. These techniques are inherently more robust than conventional optimization techniques. Incorporating a macro-macro mutation operator, namely, the jumping gene operator, inspired from biology, reduces the computational time required for convergence, significantly. It also helps to obtain the global optimal Pareto set where several Paretos exist. In this article, detailed descriptions of genetic algorithm and simulated annealing with the various jumping gene adaptations are presented and then three benchmark problems are solved using them.