Three improved hybrid metaheuristic algorithms for engineering design optimization
This paper presents three hybrid metaheuristic algorithms that further improve the two hybrid differential evolution (DE) metaheuristic algorithms described in Liao [1]. The three improved algorithms are: (i) MDE?HJ, which is a modification of MAMDE? in Liao [1] by replacing the random walk with direction exploitation local search with the Hooke and Jeeves (HJ) method; (ii) MDE?IHSHJ, which is constructed by adding the Hooke and Jeeves method to the original cooperative hybrid, i.e., MDE?IHS; and (iii) PSOMDE?HJ, which is a variation of MDE?IHSHJ by replacing improved harmony search (IHS) with particle search optimization (PSO). A comprehensive comparative study was carried out to compare the three improved hybrids with the three algorithms presented by Liao [1] in terms of average success rate, average function evaluations taken, average elapsed CPU time, and convergence profiles. A total of 18 problems, 4 more than those used in Liao [1], were selected from different engineering domains for testing. The test results indicate that all three new hybrids can achieve higher success rate in much less CPU time. Among these three hybrids, MDE?IHSHJ is the best one in terms of success rate, better than the best hybrid in Liao [1] by over 15% and better than the second best, PSOMDE?HJ, by nearly 10%.
hybrid metaheuristic differential evolution hooke and jeeves harmony search particle swarm optimization discrete-continuous constrained optimization