A Self-adaptive Artificial Bee Colony Algorithm with Incremental Population Size for Large Scale Optimization

Abstract

Large scale optimization is challenging area due to the curse of dimensionality. As a result of the increase of the problem space, computational cost becomes expensive and performance of the optimization algorithms decrease. To overcome this problem, a self-adaptive Artificial Bee Colony (ABC) algorithm called “Self-adaptive Search Equation-based Artificial Bee Colony” (SSEABC) is proposed in this paper. In SSEABC, the canonical ABC is modified with two strategies which are self-adaptive search equation determination and incremental population size. The first strategy determines the appropriate search equations for a given problem instance adaptively during execution. On the other hand, incremental population size strategy adds new food sources to the population biased towards the best-so-far solution. This leads to performance improvement. SSEABC was tested on the benchmark set provided for the special issue of Soft Computing Journal on “Scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems” (SOCO). We compared the results of the proposed algorithm to the five ABC variants and the fifteen SOCO participant algorithms. The comparison results indicate that SSEABC is more effective than the considered ABC variants and very competitive with regards to the fifteen SOCO competitor algorithms.