Memetic Artificial Bee Colony Algorithm for Large-Scale Global Optimization
Iztok Fister, Iztok Fister Jr., Janez Brest, Viljem \v{Z}umer

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
