Micro Bat Algorithm for High Dimensional Optimization Problems

Abstract

Very recently bat inspired algorithms have gained increasing attention as a powerful technique for solving optimization problems. Bat algorithm (BA) is the first one in this group. It is based on the echolocation behavior of bats. BA is very good at exploitation however it is generally poor at exploration. Dynamic Virtual Bats Algorithm (DVBA) is another bat inspired algorithm, which is proposed lately. Although the algorithm is fundamentally inspired from BA, it is conceptually very different. DVBA employs just two bats and uses role based search mechanism. It is very efficient in exploration but relatively poor in exploitation, when it comes to high dimensional problems. In this paper, a novel micro-bat algorithm (µBA) is proposed which possess the advantages of both algorithms. µBA employs a very small population compared to its classical version. It combines the swarming technique of bats in Bat Algorithm with the role based search in Dynamic Virtual Bats Algorithm. Our empirical results demonstrate that the proposed µBA achieves a good balance between exploration and exploitation. And it exhibits a better overall performance than the standard BA with larger and smaller populations on high dimensional problems.

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

“…Te results worth mentioning are those obtained with micro-Particle Swarm Optimisation (μ PSO) algorithms [76,92,93]. Further successful examples are those of micro-Artifcial Immune System (μ AIS) [94], micro-Bacterial Foraging Algorithm (μ BFA) [95], and other metaphor-led algorithms such as those in [77,96,97] (which are indeed very similar to the more established framework such as DE and PSO, thus returning similar results). Finally, other important roles played by μ EAs are to perform a local search within memetic algorithms [98] and to act as microalgorithms for multi-objective optimisation [99,100].…”