Novel hybridised variants of gravitational search algorithm for constraint optimisation
Amarjeet Singh, Kusum Deep · International Journal of Swarm Intelligence · 2017
In an earlier paper, the authors proposed three new hybridised variants of gravitational search algorithm and real coded genetic algorithms for unconstrained optimisation problems, by hybridising GSA with Laplace crossover and power mutation. Experiments on a number of test problems, including CEC 2014 benchmarks, showed that the hybridised variant incorporating both Laplace crossover and power mutation emerged a winner in terms of efficiency and reliability by increased exploration and exploitation. This paper extends the hybridised variants proposed in the above paper, for the constrained optimisation making use of the Deb's constraint handling mechanism. The performance of original GSA and the three proposed variants is investigated on a set of 24 constrained benchmark problems as given in CEC 2006. Based on a rigorous analysis of results, it is concluded that the variant hybridising GSA with Laplace crossover and power mutation outperforms all others.