Large Scale Global Optimization by Hybrid Evolutionary Computation

In management, business, economics, science, engineering, and research domains, Large Scale Global Optimization (LSGO) plays a predominant and vital role. Though LSGO is applied in many of the application domains, it is a very troublesome and a perverse task. The Congress on Evolutionary Computation (CEC) began an LSGO competition to come up with algorithms with a bunch of standard benchmark unconstrained LSGO functions. Therefore, in this paper, we propose a hybrid meta-heuristic algorithm, which combines an Improved and Modified Harmony Search (IMHS), along with a Modified Differential Evolution (MDE) with an alternate selection strategy. Harmony Search (HS) does the job of exploration and exploitation, and Differential Evolution does the job of giving a perturbation to the exploration of IMHS, as harmony search suffers from being stuck at the basin of local optimal. To judge the performance of the suggested algorithm, we compare the proposed algorithm with ten excellent meta-heuristic algorithms on fifteen LSGO benchmark functions, which have 1000 continuous decision variables, of the CEC 2013 LSGO special session. The experimental results consistently show that our proposed hybrid meta-heuristic performs statistically on par with some algorithms in a few problems, while it turned out to be the best in a couple of problems.Comment: 29 Pages, 8 figures, 9 table

Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method

Differential evolution (DE) is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS) techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE) with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS) for solving continuous unconstrained global optimization problems. The new hybrid algorithm is denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE), sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS), and differential ant-stigmergy algorithm (DASA). These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances