Parallel Hybrid Island Metaheuristic Algorithm

Abstract

This study introduces a novel Parallel Hybrid Island architecture which shows a parallel way to combine different meta-heuristic algorithms by using the island model as the base. The corresponding hybrid algorithm is called Parallel Hybrid Island Metaheuristic Algorithms (PHIMA). The hybrid parallel structure exploits the characteristics of the individual metaheuristic algorithms to boost robustness and diversity. Island Genetic Algorithm has been combined with Particle Swarm Optimization and Fireworks Algorithm to build three different PHIMA algorithms: PSO-GA (PHIMA-PGA), FWA-GA (PHIMA-FGA) and FWA-PSO-GA (PHIMA-FPGA). Further, another implementational variation known as 'co-evolution' is applied to the sub-GA islands of PHIMA-FPGA to improve the performance on multi-modal high-dimensional problems. This variation is referred to as PHIMA-FPGA-Co. Each PHIMA Algorithm exhibits different advantages and characteristics, and the parallel hybridization using the island model is found to improve robustness and population diversity. The performances of the four new algorithms are compared with each other and that of the traditional Island GAs and all four proposed PHIMA algorithms show better result quality.