Abstract
INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.
Highlights
The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems
This section briefs about the Artificial Bee Colony algorithm, proposed General Purpose Graphics Processing Unit (GPGPU) based multi-hive ABC algorithm
This paper presents a GPGPU based multi-hive ABC algorithm to solve constrained optimization problems
Summary
The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems. The widely used non-traditional techniques are nature-inspired These techniques include swarm intelligence based optimization algorithms like particle swarm optimization, ant colony optimization, artificial bee colony algorithm, etc. Each bee tries to improve its solution by neighbour search method [4]
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