A differential evolution algorithm with self-adapting strategy and control parameters.(English)Zbl 1231.90383

Summary: This paper presents a differential evolution algorithm with self-adaptive trial vector generation strategy and control parameters (SspDE) for global numerical optimization over continuous space. In the SspDE algorithm, each target individual has an associated strategy list (SL), a mutation scaling factor $$F$$ list (FL), and a crossover rate $$CR$$ list (CRL). During the evolution, a trial individual is generated by using a strategy, $$F$$, and $$CR$$ taken from the lists associated with the target vector. If the obtained trial individual is better than the target vector, the used strategy, $$F$$, and $$CR$$ will enter a winning strategy list (wSL), a winning $$F$$ list (wFL), and a winning $$CR$$ list (wCRL), respectively. After a given number of iterations, the FL, CRL or SL will be refilled at a high probability by selecting elements from wFL, wCRL and wSL or randomly generated values. In this way, both the trial vector generation strategy and its associated parameters can be gradually self-adapted to match different phases of evolution by learning from their previous successful experience. Extensive computational simulations and comparisons are carried out by employing a set of 19 benchmark problems from the literature. The computational results show that overall the SspDE algorithm performs better than the state-of-the-art differential evolution variants.

MSC:

 90C56 Derivative-free methods and methods using generalized derivatives 90C59 Approximation methods and heuristics in mathematical programming 90C26 Nonconvex programming, global optimization

CEC 05
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