Accelerating particle swarm optimization using crisscross search

Meng, Anbo; Li, Zhuan; Yin, Hao; Chen, Sizhe; Guo, Zhishan

doi:10.1016/j.ins.2015.08.018

Cited by 72 publications

(27 citation statements)

References 70 publications

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“…The constants c 1 and c 2 are positive, controlling the velocity and the displacement of the particle in the search space, providing an individual and a social search characteristic for each particle, respectively. The tendency should be to attract the particles towards the optimum or near the optimal solution of the problem by weighting the best values of the individual (pbest) and group (gbest) positions(Liang & Kang, 2016;Meng et al, 2016).The terms and represent the position and velocity of the particle in the i-th position of the d-th place of the swarm dimension. The constants terms r 1 and r 2 must be random numbers of the interval [0,1], contributing to a diversified search of possible solutions.…”

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confidence: 99%

A PLC-based Fuzzy Logic Control with Metaheuristic Tuning

Silva¹,

Marques²,

Aquino³

et al. 2019

SIC

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In recent times, artificial intelligence applications in the field of dynamic systems control have proved to be an efficient tool for the improvement of processes. Programmable Logic Controller-based control systems are found in the vast majority of industrial automation systems. In this paper, a fuzzy controller approach is proposed to enhance the design of industrial control systems using commercial PLCs. The fuzzy controller is tuned by artificial intelligent techniques, and the performances of different fuzzy controller-tuning methods are compared, including the metaheuristic ones. The results shown in this paper validate the proposal that metaheuristics tuning methods of fuzzy controller have better performance in intelligent automation.

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mentioning

confidence: 99%

A PLC-based Fuzzy Logic Control with Metaheuristic Tuning

Silva¹,

Marques²,

Aquino³

et al. 2019

SIC

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show abstract

“…PSO was proposed in 1995. Although the convergence of PSO is still controversial, its applied research has shown good results [9][10][11][12][13]. Experimental research includes optimization, biomedicine, communication, control, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Particle Swarm Optimization to Solve Large-Scale Network Plan Optimization of Resource-Leveling with a Fixed Duration

Zhang

Yang

2018

Mathematical Problems in Engineering

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Large-scale network plan optimization of resource-leveling with a fixed duration is challenging in project management. Particle swarm optimization (PSO) has provided an effective way to solve this problem in recent years. Although the previous algorithms have provided a way to accelerate the optimization of large-scale network plan by optimizing the initial particle swarm, how to more effectively accelerate the optimization of large-scale network plan with PSO is still an issue worth exploring. The main aim of this study was to develop an accelerated particle swarm optimization (APSO) for the large-scale network plan optimization of resource-leveling with a fixed duration. By adjusting the acceleration factor, the large-scale network plan optimization of resource-leveling with a fixed duration yielded a better result in this study than previously reported. Computational results demonstrated that, for the same large-scale network plan, the proposed algorithm improved the leveling criterion by 24% compared with previous solutions. APSO proposed in this study was similar in form to, but different from, particle swarm optimization with contraction factor (PSOCF). PSOCF did not have as good adaptability as APSO for network plan optimization. Accelerated convergence particle swarm optimization (ACPSO) is similar in form to the APSO proposed in this study, but its irrationality was pointed out in this study by analyzing the iterative matrix convergence.

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“…Proposed in 1995, PSO was applied to optimization, biomedicine, communication, control, plan, prediction, filter, and parameter estimation in rainfall-runoff modeling and so forth [24][25][26][27][28][29][30]. It was improved in selecting the parameter, the velocity equation of the particle, uncertainty stimulation, learning abilities, stability, convergence, and more [31][32][33][34][35][36][37][38][39][40][41]. Wang et al and Chen et al applied PSO to solve optimization of a nine-work network plan [42,43].…”

Section: Introductionmentioning

confidence: 99%

Large‐Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

Zhang

Yang

2017

Mathematical Problems in Engineering

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No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO).

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Accelerating particle swarm optimization using crisscross search

Cited by 72 publications

References 70 publications

A PLC-based Fuzzy Logic Control with Metaheuristic Tuning

A PLC-based Fuzzy Logic Control with Metaheuristic Tuning

Accelerated Particle Swarm Optimization to Solve Large-Scale Network Plan Optimization of Resource-Leveling with a Fixed Duration

Large‐Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

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