Variable mesh optimization for continuous optimization problems

Abstract Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called ‘‘variable mesh optimization’’ (VMO), in which the set of nodes (potential solutions) are distributed as a mesh. This mesh is variable, because it evolves to maintain a controlled diversity (avoiding solutions too close to each other) and to guide it to the best solutions (by a mechanism of resampling from current nodes to its best neighbour). This proposal is compared with basic population-based meta-heuristics using a benchmark of multimodal continuous functions, showing that VMO is a competitive algorithm.

• Type: Journal
• Year: 2012
• Where: SOCO
• Authors: Amilkar Puris, Rafael Bello, Daniel Molina, Francisco Herrera
• Keywords: Continuous Optimization Problems, Mesh Optimization, Meta Heuristics, SOCO 2012, Software Engineering