Comparative study of diversity based parallel dual population genetic algorithm for unconstrained function optimisations
Anantkumar J. Umbarkar, Madhuri S. Joshi, Wei‐Chiang Hong · International Journal of Bio-Inspired Computation · 2016
The genetic algorithms GAs metaheuristic deals with large scale combinatorial optimisation problems. It is biologically inspired by the method, based on the principle of survival of the fittest. In GAs, the concept of multiple populations offers an advantage of diversity. However, as the population evolves, the GA loses its diversity and sometimes it cannot avoid the local optima problem also known as premature convergence. The dual population genetic algorithm DPGA uses an extra population called the reserve population to provide additional diversity to the main population through crossbreeding. Crossbreeding solves the problem of premature convergence and helps to converge early. This paper is the empirical study of the Binary encoded parallel DPGA PDPGA. It is compared with metaheuristics given in literature based on reliability, efficacy and efficiency. The performance of PDPGA is competitive over other nature-inspired optimisation methods like genetic algorithm GA, particle swarm optimisation PSO, differential evolution DE, ANTS, bee colony, grenade explosion method GEM and bee colony optimisation BCO, but not better than artificial bee colony ABC and teaching-learning-based optimisation TLBO.