Differential Evolution: A Survey and Analysis

Abstract

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.

Differential Evolution for Neural Networks Optimization

“…As long as meta-heuristic algorithms are generally nondeterministic and not sensitive to the differentiability and continuity of the objective functions, these methods are used in a wide range of complex optimization problems. In addition, the stochastic global optimizations can identify global minimum without being trapped in local minima [19][20][21].…”

“…When the optimization problem is complex, the performance of the traditional DE algorithm depends on the selected the control parameters and mutation strategy [19,[27][28][29]. If the control parameters and selected mutation strategy are unsuitable, then DE is likely to yield premature convergence, stagnation phenomena and excessive consumption of computational resources.…”

An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution

“…Differential Evolution (DE) has been proven to be one of the most powerful global optimization algorithms [28,29]. Unfortunately, cardinal DE [18] is only applicable to unconstrained problems.…”

“…The performance of DE depends on control parameters, namely the scale factor F ∈ [0.1, 1] in (19) and the crossover rate CR ∈ [0, 1] in (20). Therefore, various parameter adaptation mechanisms have been reported [28,29,31]. ADEP employs an adaptive parameter control mechanism in which feedback from the evolutionary search is used to dynamically change the control parameters [19].…”

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

“…Computational intelligence techniques are multi-purpose artificial procedures devoted to mainly treat classification, prediction, and clustering problems. Most computational methods are inspired by interesting natural phenomena, such as inter-species communication [7,8], selection and evolution mechanisms [9], swarm intelligence [10], and evolutionary algorithms [11], among several others [12]. Artificial neural networks (ANN) are a type of bio-inspired algorithms based on how neurons operate in the brain to process data from the senses, establish memories, and control the body [13,14].…”