Abstract
Multi-objective EAs (MOEAs) are well established population-based techniques for solving various search and optimization problems. MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different problems. The use of multiple operators with a self-adaptive capability can further improve the performance of existing MOEAs. This paper suggests an enhanced version of a genetically adaptive multi-algorithm for multi-objective (AMAL-GAM) optimisation which includes differential evolution (DE), particle swarm optimization (PSO), simulated binary crossover (SBX), Pareto archive evolution strategy (PAES) and simplex crossover (SPX) for population evolution during the course of optimization. We examine the performance of this enhanced version of AMALGAM experimentally over two different test suites, the ZDT test problems and the test instances designed recently for the special session on MOEA’s competition at the Congress of Evolutionary Computing of 2009 (CEC’09). The suggested algorithm has found better approximate solutions on most test problems in terms of inverted generational distance (IGD) as the metric indicator.
Highlights
Multi-objective EAs (MOEAs) are well established population-based techniques for solving various search and optimization problems
The main focus is on the decision space while in multi-objective optimization, the focus is mainly on the objective space because objective values are used in checking for optimality [43]
Our main objective is to further improve the algorithmic performance of ALMAGAM by employing by employing multiple search operators including the differential evolution (DE) [46], particle swarm optimization (PSO) [15], simulated binary crossover (SBX) [24], Pareto archive evolution strategy (PAES) [23] and simplex crossover (SPX) [50]
Summary
Multi-objective evolutionary optimization is a subject of intense interest in all fields of Science, Engineering, Economics, Logistics and others. A genetically adaptive multi-algorithm for multi-objective (AMALGAM) optimisation is recently developed for solving both multi-objective optimization problems [52] and single optimization problems [53] It employs multiple search operators for its population evolution. MOEA/D [54] decomposes the approximated PF of the given MOP into a number of different single objective optimization subproblems (SOPs) It optimizes all SOPs simultaneously using generic evolutionary algorithm. Our main objective is to further improve the algorithmic performance of ALMAGAM by employing by employing multiple search operators including the differential evolution (DE) [46], particle swarm optimization (PSO) [15], simulated binary crossover (SBX) [24], Pareto archive evolution strategy (PAES) [23] and simplex crossover (SPX) [50].
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