A Novel Multi-objective Orthogonal Simulated Annealing Algorithm for Solving Multi-objective Optimization Problems with a Large

In this paper, a novel multi-objective orthogonal simulated annealing algorithm MOOSA using a generalized Pareto-based scale-independent fitness function and multi-objective intelligent generation mechanism (MOIGM) is proposed to efficiently solve multi-objective optimization problems with large parameters. Instead of generate-and-test methods, MOIGM makes use of a systematic reasoning ability of orthogonal experimental design to efficiently search for a set of Pareto solutions. It is shown empirically that MOOSA is comparable to some existing population-based algorithms in solving some multi-objective test functions with a large number of parameters.

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Li-Sun Shu, Shinn-Jang Ho, Shinn-Ying Ho, Jian-Hung Chen, Ming-Hao Hung
• Keywords: Annealing Algorithm Moosa, GECCO 2004, Intelligent Generation Mechanism, Systematic Reasoning Ability