Particle Swarm Optimization for multimodal combinatorial problems and its application to protein design

Grecia Lapizco-Encinas, Carl Kingsford, James A. Reggia

Abstract: Particle Swarm Optimization (PSO) is a well-known technique for numerical optimization with real-parameter representation. Like other meta-heuristics, PSO is usually designed for the goal of finding a single optimal solution for a given problem. However, many scientific and engineering optimization problems have convoluted search spaces with a large number of optima. This paper explores the ability of a cooperative combinatorial PSO (CCPSO) used in tandem with explicit diversity strategies to discover sets of high-quality and diverse solutions. This idea has been pursued in numerical optimization in several PSO variants, but no explicit PSO has been developed to handle multimodal combinatorial problems. A protein sequence redesign problem is selected to assess the exploratory ability of multimodal CCPSO by evaluating both the quality and diversity of the solutions obtained.