Abstract
Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals in the current population are selected and stored into a new archive; second, a debating mutation strategy is adopted in terms of the updating status of the current population to decide the parent’s selection. Both of the two methods can improve the performance of DE. We conducted numerical experiments based on various functions from CEC 2014, where the results demonstrated excellent performance of this algorithm. Furthermore, this algorithm is applied to the real-world optimization problem of the four-bar linkages, where the results show that the performance of RUSDE is better than other algorithms.
Highlights
Algorithm with a Rank-Up Selection: In real-world optimization problems, people have proposed various algorithms inspired by evolutions of lives, social behaviors, or physical phenomena, e.g., Genetic Algorithm (GA) [1], Ant Colony Optimization Algorithm (ACO) [2], Particle Swarm Optimization (PSO) [3], Cuckoo Search (CS) [4], Teach-Learning-Based Optimization (TLBO) [5,6,7], Krill Herd (KH) [8], Backtracking Search Optimization Algorithm (BSA) [9] and differential evolution (DE) [10]
The main idea of the proposed framework contains two parts: one is that an archive is created during the process which consists of the rank-up individuals, the other one is that a learning strategy has been adopted in terms of the continuous updating numbers of the top-rank individuals and the current individual
We proposed an enhanced learning method to improve the performance of differential evolution (DE), named RUSDE
Summary
Algorithm with a Rank-Up Selection: In real-world optimization problems, people have proposed various algorithms inspired by evolutions of lives, social behaviors, or physical phenomena, e.g., Genetic Algorithm (GA) [1], Ant Colony Optimization Algorithm (ACO) [2], Particle Swarm Optimization (PSO) [3], Cuckoo Search (CS) [4], Teach-Learning-Based Optimization (TLBO) [5,6,7], Krill Herd (KH) [8], Backtracking Search Optimization Algorithm (BSA) [9] and differential evolution (DE) [10]. Guo improved the optimal archive with all updating individuals and built a new Successful-Parent-Selecting framework (SPSDE) [29] Later, she introduced the concept of eigenvector and proposed a new crossover method (EIGDE) [30]. Plenty of algorithms adopt the two learning strategies: The individuals with low ranking learn from the ones with high ranking, such as rank-jDE [31] and TLBO [5]; the individuals without updating learn from the ones with updating, such as JADE [27], SHADE [28], and SPSDE [29] These two learning strategies had been proved to be valid in improving the performance of DE. The main idea of the proposed framework contains two parts: one is that an archive is created during the process which consists of the rank-up individuals, the other one is that a learning strategy has been adopted in terms of the continuous updating numbers of the top-rank individuals and the current individual.
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