Differential evolution and quantum-inquired differential evolution for evolving Takagi–Sugeno fuzzy models

Su, Haijun; Yang, Yupu

doi:10.1016/j.eswa.2010.11.107

Cited by 33 publications

(11 citation statements)

References 15 publications

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“…The results obtained by using different algorithms are shown in Table 6. [37] 0.129 -Proposed by Pomares et al [37] 0.363 -DE/QDE [37] 0.112 -The present study 0.111 0.09428%…”

Section: Experiments On Egtmentioning

confidence: 81%

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

et al. 2015

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“…The results obtained by using different algorithms are shown in Table 6. [37] 0.129 -Proposed by Pomares et al [37] 0.363 -DE/QDE [37] 0.112 -The present study 0.111 0.09428%…”

Section: Experiments On Egtmentioning

confidence: 81%

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

et al. 2015

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“…In DEA that is a population based heuristic method presented by Storn and Price [26], [27], the differences between the solutions are used for the production of the new possible solutions. The improvement of the solutions is accomplished by using crossover, mutation, and selection operations [20]. The mutation operator rather than crossover operation has an effective role in the performance of the algorithm.…”

Section: Differential Evolution Algorithm (Dea)mentioning

confidence: 99%

“…The use of simulated annealing algorithm to optimize the membership functions of Takagi-Sugeno type rules was investigated by Guely et al [18]. To learn the Takagi-Sugeno fuzzy model parameters, Su and Yang [20] proposed a DEA based modelling approach. In the study of Habbi et al [23], an ABC based approach to obtain the structures and parameters of the Takagi-Sugeno type fuzzy systems was reported.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony Algorithms for Fuzzy Modelling of Nonlinear Systems

Konar¹,

Bağış²

2016

ElAEE

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This paper presents the results of the nonlinear system modelling approach based on the use of fuzzy rules optimized by different population based optimization algorithms. Fuzzy rule based models with different number of the rules are used to describe the some nonlinear systems in the literature. Firstly, parameters of the fuzzy models are determined by the artificial bee colony (ABC) algorithm. To demonstrate the efficiency of the ABC algorithm, its modelling ability is compared with the other two powerful population based algorithms, particle swarm optimization (PSO) and differential evolution algorithm (DEA). Simulation results show that a successful model performance with good description ability in the modelling of nonlinear or complex systems can be obtained by using one of the population based algorithms in design of the fuzzy rule based models.

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“…Su et al [32] used a quantuminspired differential evolution (DE) algorithm to discover the classification rules. Similarly, Su and Yang [33] proposed DE/QDE to learn the Takagi-Sugeno (T-S) fuzzy model. Typically speaking, the quantum mechanism is the most favored to be integrated with the swarm algorithm.…”

Section: The Development Of Qeamentioning

confidence: 99%

A New Improved Quantum Evolution Algorithm with Local Search Procedure for Capacitated Vehicle Routing Problem

Cui

Wang

Deng

et al. 2013

Mathematical Problems in Engineering

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The capacitated vehicle routing problem (CVRP) is the most classical vehicle routing problem (VRP); many solution techniques are proposed to find its better answer. In this paper, a new improved quantum evolution algorithm (IQEA) with a mixed local search procedure is proposed for solving CVRPs. First, an IQEA with a double chain quantum chromosome, new quantum rotation schemes, and self-adaptive quantum Not gate is constructed to initialize and generate feasible solutions. Then, to further strengthen IQEA's searching ability, three local search procedures 1-1 exchange, 1-0 exchange, and 2-OPT, are adopted. Experiments on a small case have been conducted to analyze the sensitivity of main parameters and compare the performances of the IQEA with different local search strategies. Together with results from the testing of CVRP benchmarks, the superiorities of the proposed algorithm over the PSO, SR-1, and SR-2 have been demonstrated. At last, a profound analysis of the experimental results is presented and some suggestions on future researches are given.

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Differential evolution and quantum-inquired differential evolution for evolving Takagi–Sugeno fuzzy models

Cited by 33 publications

References 15 publications

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

Performance Comparison of Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony Algorithms for Fuzzy Modelling of Nonlinear Systems

A New Improved Quantum Evolution Algorithm with Local Search Procedure for Capacitated Vehicle Routing Problem

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