Fast Computational Efficient Directional Shrinking Search Optimization Algorithm

doi:10.23940/ijpe.21.06.p7.543551

Abstract

Abstract: In this paper, a novel fast heuristic optimization algorithm has been proposed that effectively boosts the exploration and exploitation process during the search of a global optimum in the search region. In this proposed algorithm, a particle position dependent stochastic variable is used to control the exploration and exploitation process. It is also used to reduce the computational time requirement. The search region shrinks efficiently in a continuous manner during successive iterations. The performance of the algorithm has been validated by comparing the simulation results of the particle swarm optimization (PSO), quantum particle swarm optimization (QPSO) and firefly algorithm (FFA) using some well-known benchmark functions. The proposed algorithm reduces the computational time around 44.2% compared to others in finding the global optimum point.

Key words: heuristic optimization algorithm, particle swarm optimization (PSO), quantum particle swarm optimization (QPSO), firefly algorithm (FFA), benchmark functions

D.P. Tripathi, Mahesh Nayak, Rajaboina Manoj, Surarapu Sudheer, and K. Praghash. Fast Computational Efficient Directional Shrinking Search Optimization Algorithm [J]. Int J Performability Eng, 2021, 17(6): 543-551.

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