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Three new stochastic local search algorithms for continuous optimization problems

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Abstract

This paper introduces three new stochastic local search metaheuristics algorithms namely, the Best Performance Algorithm (BPA), the Iterative Best Performance Algorithm (IBPA) and the Largest Absolute Difference Algorithm (LADA). BPA and IBPA are based on the competitive nature of professional athletes, in them desiring to improve on their best recorded performances. LADA is modeled on calculating the absolute difference between two numbers. The performances of the algorithms have been tested on a large collection of benchmark unconstrained continuous optimization functions. They were benchmarked against two well-known local-search metaheuristics namely, Tabu Search (TS) and Simulated Annealing (SA). Results obtained show that each of the new algorithms delivers higher percentages of the best and mean function values found, compared to both TS and SA. The execution times of these new algorithms are also comparable. LADA gives the best performance in terms of execution time.

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  1. School of Mathematics, Statistics and Computer Science, University of Kwa-Zulu Natal, University Road, Westville, Private Bag X 54001, Durban, 4000, South Africa

    Sivashan Chetty & Aderemi Oluyinka Adewumi

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  1. Sivashan Chetty
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  2. Aderemi Oluyinka Adewumi
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Correspondence to Aderemi Oluyinka Adewumi.

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Chetty, S., Adewumi, A.O. Three new stochastic local search algorithms for continuous optimization problems. Comput Optim Appl 56, 675–721 (2013). https://doi.org/10.1007/s10589-013-9566-3

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