PSEUDO-RANDOM DIRECTIONAL SEARCH: A NEW HEURISTIC FOR OPTIMIZATION

Meta-heuristics have already received considerable attention in various fields of engineering optimization problems. Each of them employes some key features best suited for a specific class of problems due to its type of search space and constraints. The present work develops a Pseudo-random Directional Search, PDS, for adaptive combination of such heuristic operators. It utilizes a short term memory via indirect information share between search agents and the directional search inspired by natural swarms. Treated numerical examples illustrate the PDS performance in continuous and discrete design spaces.