Hybridizing Cuckoo Search with Bio-inspired Algorithms for Constrained Optimization Problems

Constrained optimization problems are complex and highly nonlinear, optimal solutions of practical interest may not even exist. This paper investigates the hybridization of a standard Cuckoo search (CS) algorithm with genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. A new hybrid algorithms by adding positive properties of GA and PSO to the CS algorithms (denoted as CS-GA and CS-PSO, respectively) are proposed to solve for constrained optimization problems. According to the life style of cuckoo birds, each cuckoo will lay more than one egg at a time and always searching a better place to lay the eggs not to be discovered by the host birds, in order to increase the chance of eggs survival rate. By including evolution principles of GA or swarm intelligence of PSO in CS, it is possible to increase the optimization search space. The performance of hybrid algorithms developed in this paper is first tested with a well-known Himmelblau's function and then further validated by solving four classical constrained optimization problems. Optimization results fully demonstrate the efficiency of the proposed approaches.