Comparative study of diversity based parallel dual population genetic algorithm for unconstrained function optimisations
by A.J. Umbarkar; Madhuri S. Joshi; Wei-Chiang Hong
International Journal of Bio-Inspired Computation (IJBIC), Vol. 8, No. 4, 2016

Abstract: 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).

Online publication date: Tue, 30-Aug-2016

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