Memetic Artificial Bee Colony Algorithm for Large-Scale Global Optimization

TL;DR

This paper introduces a hybrid memetic artificial bee colony algorithm that combines local search heuristics for improved large-scale global optimization, demonstrating competitive performance against existing methods.

Contribution

The study develops a novel memetic ABC algorithm hybridized with Nelder-Mead and RWDE heuristics, effectively balancing exploration and exploitation for large-scale problems.

Findings

Comparable results to DECC-G, DECC-G*, and MLCC

Effective balancing of exploration and exploitation

Successful application to large-scale optimization

Abstract

Memetic computation (MC) has emerged recently as a new paradigm of efficient algorithms for solving the hardest optimization problems. On the other hand, artificial bees colony (ABC) algorithms demonstrate good performances when solving continuous and combinatorial optimization problems. This study tries to use these technologies under the same roof. As a result, a memetic ABC (MABC) algorithm has been developed that is hybridized with two local search heuristics: the Nelder-Mead algorithm (NMA) and the random walk with direction exploitation (RWDE). The former is attended more towards exploration, while the latter more towards exploitation of the search space. The stochastic adaptation rule was employed in order to control the balancing between exploration and exploitation. This MABC algorithm was applied to a Special suite on Large Scale Continuous Global Optimization at the 2012 IEEE Congress on Evolutionary Computation. The obtained results the MABC are comparable with the results of DECC-G, DECC-G*, and MLCC.