Abstract
Cuckoo search algorithm (CS) is a powerful biological-inspired search algorithm, which is widely used in continuous space optimization problems. However, a single search strategy in CS makes all cuckoos have similar search behavior, and it is liable to plunges into local optimal. In addition, whether CS can successfully solve a problem largely depends on the value of control parameters. Using the trial and error method to determine the value of parameters will cost a lot of computational expense frequently. In order to solve these problems, a multi-strategy adaptive cuckoo algorithm (MSACS) is proposed in this paper. Firstly, five search strategies are adapted to cooperate with each other, and the use of various previous strategies and control parameters are studied. The probability of each strategy being used and the value of control parameters are changed adaptively. Then, the performance of MSACS is tested and evaluated on 24 common benchmark functions. Finally, several advanced CS algorithms, particle swarm algorithm (PSO) and differential evolution algorithm (DE) variants will be compared with MSACS. The results show that the MSACS is better than the algorithms above.
Highlights
In recent decades, people have proposed a series of new heuristic algorithms by observing the behavior of biological populations: swarm intelligence algorithm
Many improved cuckoo algorithms have been proposed. These improved cuckoo algorithms are divided into two categories: (I)Improving the Control Parameters and Levy Flight Strategy: Walton et al proposed an improved Cuckoo search algorithm [20], which reduced the value of control parameters in a nonlinear way, and put the best part of the solution into a top-level set, randomly selected two cuckoos in each top-level sets
This paper proposes a multi-strategy adaptive cuckoo algorithm to improve the Cuckoo search algorithm (CS) algorithm from the following two aspects: (i)Using multiple strategies to search with different step size
Summary
People have proposed a series of new heuristic algorithms by observing the behavior of biological populations: swarm intelligence algorithm. (I)Improving the Control Parameters and Levy Flight Strategy: Walton et al proposed an improved Cuckoo search algorithm [20], which reduced the value of control parameters in a nonlinear way, and put the best part of the solution into a top-level set, randomly selected two cuckoos in each top-level sets. Where ξ is a random number subject to Gaussian distribution with a variance of 0.3 and a mean of 0.5, XL1,G is a randomly selected individual in LM This strategy makes MSACS memorable and prevents most cuckoos from getting stuck in a few local minima that are difficult to jump out. The higher the mi,j j=G−LM success rate of strategy i, and the more likely it will be used in the future
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