Differential Evolution, an Alternative Approach to Evolutionary Algorithm

Abstract

As a relatively new population based optimization technique, differential evolution has been attracting increasing attention for a wide variety of engineering applications including power engineering. Unlike the conventional evolutionary algorithms which depend on predefined probability distribution function for mutation process, differential evolution uses the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently, the object vectors' differences will pass the objective functions topographical information toward the optimization process, and therefore provide more efficient global optimization capability. This paper aims at providing an overview of differential evolution and presenting it as an alternative to evolutionary algorithms with two application examples in power systems.

Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem

“…In this article, three of the evolutionary computing techniques, viz. (i) genetic algorithm (GA) [8], (ii) differential evolution (DE) [11], and (iii) particle swarm optimization (PSO) [14], are implemented for addressing the portfolio optimization problem. For both the genetic algorithm (GA) and differential evolution (DE), advanced crossover techniques are used, viz.…”

“…Differential Evolution (DE) [11][12] is a member of the Evolutionary Algorithm (EA) family, which is known to be more accurate, faster, and more reliable for opti-mization problems. It is easy to comprehend, similar to the Genetic Algorithm (GA) [8], and can be used for portfolio optimization.…”

“…PSO [14][95] uses the technique of "gbest neighborhood topology" as proposed by Kennedy et al [96]. In multiple research studies, it has been proven that PSO outperforms other evolutionary techniques such as GA [8] and DE [11][12] and therefore has been used in this work. PSO has several advantages over other evolutionary techniques and has been applied in areas involving function optimization, artificial neural network training, fuzzy control systems, and fields related to finance and accounting.…”

“…In recent times, various versions of quantum-inspired metaheuristics have been developed to create quantum-inspired versions of classical metaheuristics, such as the genetic algorithm [124], tabu search [125], differential evolution [11][12], particle swarm optimization [14], and ant colony optimization [126] to be used in quantum space. Examples of these include the quantum-inspired genetic algorithm [127][128] [129], quantum-inspired tabu search [130][131] [132], quantuminspired differential evolution [133], quantum-inspired particle swarm optimization [134][135], and quantum-inspired ant colony optimization [136][137].…”

“…(i) arithmetic crossover and (ii) heuristic crossover techniques, are implemented, and the results of application on the mentioned datasets are reported. • Differential evolution (DE): Differential evolution (DE) [11] [12] is another population-based metaheuristic algorithm, similar to GA, and is generally found to be faster and more accurate than GA. As a part of this study, DE is studied in detail with two popular crossover techniques, viz., (i) arithmetic crossover and (ii) heuristic crossover techniques, are implemented, and the results of application on the mentioned datasets are reported. • Particle swarm optimization (PSO): Particle swarm optimization (PSO) [13] [14] is the third evolutionary technique considered for this study.…”