Abstract
Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.
Highlights
We constructed a model of cuckoo search (CS) with Q-Learning and genetic operators, and solved the address of logistics distribution center with DMQL-CS algorithm in which adopts Q-Learning scheme to learn the individual optimal step size strategy according to the effect of individual multi-steps
The most appropriate step size control strategy is chosen as a parameter for the current step size evolution of the cuckoo, which increases the adaptability of individual evolution
To accelerate the convergence of the algorithm, genetic operators and hybrid operations are added to DMQL-CS algorithm
Summary
Optimization problems have been one of the most important research topics in recent years. Li et al [71] enhanced the exploitation ability of the cuckoo search algorithm by using an orthogonal learning strategy. We present an improved CS algorithm called dynamic step size cuckoo search algorithm (DMQL-CS). Q-Learning considers the multi-step evolution effect of individual such that the most appropriate step size control strategies are retained for the generation. In the DMQL-CS algorithm, according to multi-step effect of individual for a few steps forward, the optimal step size control strategy is learned. We introduce the designed crossover operation into problem of logistics distribution center location in this paper, which determines the performance of the algorithm to some extent. To improve the search ability of the CS algorithm, numerous strategies have been designed to adjust the crossover rate.
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