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Three new stochastic local search algorithms for continuous optimization problems. (English) Zbl 1287.90065

Summary: 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 are tested on a large collection of benchmark unconstrained continuous optimization functions. They are benchmarked against two well-known local-search metaheuristics, namely, the tabu search (TS) and the 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.

MSC:

90C30 Nonlinear programming
90C59 Approximation methods and heuristics in mathematical programming
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