A systematic overview of developments in differential evolution and particle swarm optimization with their advanced suggestion

Parouha, Raghav Prasad; Verma, Pooja

doi:10.1007/s10489-021-02803-7

Cited by 18 publications

(12 citation statements)

References 128 publications

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“…DE is one of the most popular evolutionary algorithms used to solve optimization problems because it is simple, robust and computationally efficient. However, DE faces issues in convergence rate and local exploitation rate [9]. Therefore, various research efforts for its improvement are continuously carried out even though the algorithm has been introduced 25 years ago by Storn and Price [10].…”

Section: Improved Differential Evolutionmentioning

confidence: 99%

“…Therefore, various research efforts for its improvement are continuously carried out even though the algorithm has been introduced 25 years ago by Storn and Price [10]. Besides, surveys related DE variants are updated after a certain years to accommodate various modifications of DEs, as shown in [5,9,[11][12][13][14][15]. The surveys show that most of the research efforts focus on improving the performance of DE through parameter settings and modifications in genetic operations consisting of initialization, differential mutation, crossover and selection.…”

Section: Improved Differential Evolutionmentioning

confidence: 99%

“…The surveys show that most of the research efforts focus on improving the performance of DE through parameter settings and modifications in genetic operations consisting of initialization, differential mutation, crossover and selection. Another potential direction towards improving DE is the use of hybridization, and it has gained research attention [9,11,12].…”

Section: Improved Differential Evolutionmentioning

confidence: 99%

“…Most of the optimization algorithms are not able to solve a variety of problems [9]. However, continuous research efforts are required to improve the algorithm through the complementary of their advantages.…”

Section: Improved Differential Evolutionmentioning

confidence: 99%

“…It is implemented to enhance its performance and overcome its limitations such as convergence speed [11], premature convergence [9] and local minima [9]. The types of algorithms used for hybridization in DE can either belong to the same or different categories of algorithms.…”

Section: Improved Differential Evolutionmentioning

confidence: 99%

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A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

et al. 2022

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Evolutionary computation algorithms (EC) and swarm intelligence have been widely used to solve global optimization problems. The optimal solution for an optimization problem is called by different terms in EC and swarm intelligence. It is called individual in EC and particle in swarm intelligence. Self-adaptive differential evolution (SaDE) is one of the promising variants of EC for solving global optimization problems. Adapting self-manipulating parameter values into SaDE can overcome the burden of choosing suitable parameter values to create the next best generation’s individuals to achieve optimal convergence. In this paper, a fully informed particle swarm (FIPS) is hybridized with SaDE to enhance SaDE’s exploitation capability while maintaining its exploration power so that it is not trapped in stagnation. The proposed hybrid is called FIPSaDE. FIPS, a variant of particle swarm optimization (PSO), aims to help solutions jump out of stagnation by gathering knowledge about its neighborhood’s solutions. Each solution in the FIPS swarm is influenced by a group of solutions in its neighborhood, rather than by the best position it has visited. Indirectly, FIPS increases the diversity of the swarm. The proposed algorithm is tested on benchmark test functions from “CEC 2005 Special Session on Real-Parameter Optimization” with various properties. Experimental results show that the FIPSaDE is more effective and reasonably competent than its standalone variants, FIPS and SaDE, in solving the test functions, considering the solutions’ quality.

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Section: Improved Differential Evolutionmentioning

confidence: 99%