Ensemble particle swarm optimization and differential evolution with alternative mutation method

Wang, Hong; Zuo, Lulu; Liu, Jing; Yi, Wenjie; Niu, Ben

doi:10.1007/s11047-018-9712-z

Cited by 17 publications

(6 citation statements)

References 35 publications

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“…the process of evolution and the intelligentsia shown by its various species starting from micro-organisms like bacteria to birds, honey bees, flies, fishes, frogs, monkeys, wolfs and many more [81]. The popular and widely applied metaheuristic algorithms include Genetic Algorithms (GAs) [33,73,75], Particle Swarm Optimization algorithm (PSO) [41,42,59,60,65], Artificial Bee Colony algorithm (ABC) [25], Cuckoo Search algorithm (CS) [15,19,26,35], Firefly Algorithm (FA) [11,20,32,37,46], Differential evolution (DE) algorithms [25,29,30,85], Ant colony optimization(ACO) [55] etc. Though more than 300 types of nature inspired metaheuristic algorithms are available in the literature [82], the most widely accepted and popular primary algorithms mainly include the Genetic algorithms(GAs), Differential evolution algorithms(DE), Artificial Bee colony algorithms(ABC), particle swarm optimization algorithms(PSO), fireflies algorithm(FA) and ant colony optimization algorithm(ACO).…”

Section: Literature Reviewmentioning

confidence: 99%

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

et al. 2020

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In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FADE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm's performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem.

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Section: Literature Reviewmentioning

confidence: 99%

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

et al. 2020

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“…Literature [9] proposed a PSO-based differential evolution algorithm DEPSO to improve the optimization performance of DE. The method adopts improved mutation strategy and PSO mutation strategy.…”

Section: Introductionmentioning

confidence: 99%

Differential adaptive SA-DEPSO algorithm based on particle swarm

Peng

Zhao

2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

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The differential evolution algorithm is a random search algorithm. Aiming at the problems of premature convergence and slow optimization in differential evolution algorithm, a differential adaptive SA-DEPSO algorithm based on particle swarm optimization is proposed. First, the positioning problem is transformed into a function iteration optimization problem by using the least square method. Then the adaptive differential evolution strategy is fused on the basis of the particle swarm optimization algorithm. This algorithm can not only avoid the problem of premature convergence, but also improve the optimization speed and reduce the positioning error. Simulation analysis shows that when the number of iterations reaches 40, the algorithm in this paper reaches the optimal value and converges, saving the optimization time. Compared with DEPSO, SA-MCDE and literature 11, the average number of optimization runs is reduced by 75, 55 and 25 times; the average positioning error of the algorithm in this paper is reduced by 17.3%, 13.1% and 7.5% respectively.

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“…To overcome the limitations of a single algorithm, scholars have combined PSO and DE and conducted much research on this kind of hybrid algorithms, such as selfadaptive mutation DE based on PSO (DEPSO) [46], ensemble PSO and DE (EPSODE) [47], combining DE and PSO (DEPSO) [48], and hybridizing PSO with DE (PSO-DE) [49]. e existing hybrid algorithms can be roughly classified as follows: two algorithms are used in sequence, two algorithms solve and communicate with two different populations, respectively, and two algorithms evaluate and select optimal values.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

2021

Mobile Information Systems

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Particle swarm optimization (PSO) is a common metaheuristic algorithm. However, when dealing with practical engineering structure optimization problems, it is prone to premature convergence during the search process and falls into a local optimum. To strengthen its performance, combining several ideas of the differential evolution algorithm (DE), a dynamic probability mutation particle swarm optimization with chaotic inertia weight (CWDEPSO) is proposed. The main improvements are achieved by improving the parameters and algorithm mechanism in this paper. The former proposes a novel inverse tangent chaotic inertia weight and sine learning factors. Besides, the scaling factor and crossover probability are improved by random distributions, respectively. The latter introduces a monitoring mechanism. By monitoring the convergence of PSO, a developed mutation operator with a more reliable local search capability is adopted and increases population diversity to help PSO escape from the local optimum effectively. To evaluate the effectiveness of the CWDEPSO algorithm, 24 benchmark functions and two groups of engineering optimization experiments are used for numerical and engineering optimization, respectively. The results indicate CWDEPSO offers better convergence accuracy and speed compared with some well-known metaheuristic algorithms.

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Ensemble particle swarm optimization and differential evolution with alternative mutation method

Cited by 17 publications

References 35 publications

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

Differential adaptive SA-DEPSO algorithm based on particle swarm

A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

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