An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

Abstract

Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multipopulation artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC.Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results.

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

“…Recently, the number of swarms was adapted [147] using a database that collects heuristic information of the algorithm behaviour changes to better track multiple optima. Nseef et al [170] proposed a multi-colony ABC algorithm. The number of colonies is adaptively maintained based on the dynamic change strength.…”

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

“…Xiang et al proposed a dynamic, multi-colony, multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy [29]. Nseef et al put forward an adaptive multi-population artificial bee colony (ABC) algorithm for dynamic optimization problems (DOPs) [30]. The experimental results show that compared with the traditional multi-objective algorithms, these variants of Multi-objective ABC can find solutions with competitive convergence and diversity within a shorter period of time.…”

An Adaptive Multi-population Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problem

“…An adaptive multisubpopulation competition (AMC) and multiniche crowding are proposed and incorporated into a memetic algorithm in Sheng W et al [11]. Nseef S K et al [12] and Zhang M et al [13] introduced the mulit-population artificial bee colony algorithm. The method divides the population into server subpopulation and transfers the information among all the subpopulation.…”