Large Scale Global Optimization by Hybrid Evolutionary Computation

TL;DR

This paper introduces a hybrid meta-heuristic combining Improved and Modified Harmony Search with Modified Differential Evolution to effectively solve large-scale global optimization problems, outperforming several existing algorithms on benchmark tests.

Contribution

The paper presents a novel hybrid algorithm integrating IMHS and MDE with an alternate selection strategy for large-scale optimization, demonstrating superior performance on benchmark functions.

Findings

The proposed hybrid algorithm performs statistically on par with top algorithms.

It is the best performer on some benchmark problems.

The method effectively balances exploration and exploitation.

Abstract

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.