Wenchao Yi, Zhilei Lin, Yong Chen, Zhi Pei^*, Jiansha Lu

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2841-2860, 2023, DOI:10.32604/cmes.2023.027055

Abstract Effective constrained optimization algorithms have been proposed for engineering problems recently. It is common to consider constraint violation and optimization algorithm as two separate parts. In this study, a pbest selection mechanism is proposed to integrate the current mutation strategy in constrained optimization problems. Based on the improved pbest selection method, an adaptive differential evolution approach is proposed, which helps the population jump out of the infeasible region. If all the individuals are infeasible, the top 5% of infeasible individuals are selected. In addition, a modified truncated ε-level method is proposed to avoid trapping in infeasible regions. The proposed adaptive… More > Graphic Abstract

Qamar Abbas¹, Khalid Mahmood Malik², Abdul Khader Jilani Saudagar^3,*, Muhammad Badruddin Khan³, Mozaherul Hoque Abul Hasanat³, Abdullah AlTameem³, Mohammed AlKhathami³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1229-1250, 2022, DOI:10.32604/cmc.2022.024211

Abstract One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive… More >

Ti-Hung Chen¹, Ming-Feng Yeh^2,*

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 407-420, 2020, DOI:10.32604/iasc.2020.013917

Abstract On the basis of JADE (adaptive differential evolution with optional external archive) and the modified differential evolution with p-best crossover (MDE_pBX), this study attempts to propose a modified mutation strategy termed "DE/(pbest)/1" for the differential evolution (DE) algorithm, where “(pbest)” represents the mean of p top-best vectors. Two modified parameter adaptation mechanisms are also proposed to update the crossover rate and the scale factor, respectively, in an adaptive manner. The DE variant with the proposed mutation strategy and two modified adaptation mechanisms is termed adaptive differential evolution with mean-of-pbest mutation strategy, denoted by ADE_pBM is comparable to or better than… More >

F. S. Lobato¹, V. Steffen Jr², A. J. Silva Neto³

CMES-Computer Modeling in Engineering & Sciences, Vol.69, No.1, pp. 1-18, 2010, DOI:10.3970/cmes.2010.069.001

Abstract Differential Evolution Algorithm (DE) has shown to be a powerful evolutionary algorithm for global optimization in a variety of real world problems. DE differs from other evolutionary algorithms in the mutation and recombination phases. Unlike some other meta-heuristic techniques such as genetic algorithms and evolutionary strategies, where perturbation occurs in accordance with a random quantity, DE uses weighted differences between solution vectors to perturb the population. Although the efficiency of DE algorithm has been proven in the literature, studies indicate that the efficiency of the DE methods is sensitive to its control parameters (perturbation rate and crossover rate) and there… More >