Design of chemo-GA for engineering design optimization problem

Abstract

This paper proposes a novel hybridized algorithm to solve Engineering Design optimization problem. The algorithm is named as Chemo-GA for constrained optimization (CGAC) which hybridizes Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO). The better performance of CGAC is realized over some recent techniques reported in the literature through a test bed of 7 benchmark functions. The algorithm is compared with LXPMC and HLXPMC. In, LXPM Laplace crossover (LX) and power mutation (PM) are used. The hybridization of LXPM with Quadratic Approximation (QA) operator is called HLXPMC. Further, 1 typical engineering problem is solved by CGAC and the numerical result is compared with recent state-of-the art algorithm. The outperformance of CGAC is realized from the computational results.