A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization

Liu, Ruochen; Li, Jianxia; Fan, Jing; Mu, Caihong; Jiao, Licheng

doi:10.1016/j.ejor.2017.03.048

Cited by 115 publications

(33 citation statements)

References 33 publications

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“…In order to solve the problem of multi-period optimization like the problem in this paper, many dynamic particle swarm optimization algorithms are proposed. Liu et al proposed a multi-swarm particle swarm optimization algorithm to deal with dynamic optimization problem [36]. In their algorithm, a similarity detection operator was used to detect the change of the problem, and a memory based dynamic mechanism was adopted to response to the change.…”

Section: Solution Algorithmmentioning

confidence: 99%

Improved Dynamic PSO-Based Algorithm for Critical Spare Parts Supply Optimization Under (T, S) Inventory Policy

Wang

Shi

2019

IEEE Access

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This paper investigates the multi-period and multi-echelon spare parts supply optimization problem. The periodic review order-up-to-S inventory policy is considered to determine the supply timing and the spare parts consumption. The spare parts consumption in lead time of each customer is calculated by the process of spare parts supply. A dynamic optimization mathematical model is developed to find the optimal supply scheme in each period with the objective of minimum total costs. In the dynamic optimization model, the parameters are changed with time. An improved particle swarm optimization algorithm is proposed to solve this dynamic problem. In the proposed algorithm, a change detector and a change response strategy are used to handle the change of optimization environment. We also adopt a selfadaption neighborhood search strategy to improve the ability of exploration and exploitation of the algorithm. Numerical experiment shows that the proposed strategy can improve the performance of algorithm greatly, and the model and solution method in this paper can be applied to optimize the spare parts supply in various scenarios.

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Section: Solution Algorithmmentioning

confidence: 99%

Improved Dynamic PSO-Based Algorithm for Critical Spare Parts Supply Optimization Under (T, S) Inventory Policy

Wang

Shi

2019

IEEE Access

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“…In contrast to this paper, the framework requires the availability of a simulator in order to optimize the policies and so the methods presented in [20] are not directly applicable to our problem. Some studies [21], [22], [23] focus on optimization problems where the optima can shift over time. The main differences between our problem and the problems considered in these studies are that: (i) only one function evaluation is allowed in our setting (one reserve price each time period); (ii) after each function evaluation we get a noisy estimate of the objective value; (iii) in standard PSO problem settings the objective function is known and may shift over time, whereas in our setting there is no explicitly known objective function that can be used in order to directly maximize revenue.…”

Section: Related Literaturementioning

confidence: 99%

A PSO-based Algorithm for Reserve Price Optimization in Online Ad Auctions

Rhuggenaath

Akçay

Zhang

et al. 2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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“…An environment factor is added to the velocity adjustment in PSO to improve the global searching ability, which is represented by the cluster centers of the partitioning results. Liu et al [14] developed a modified coevolutionary multiswarm optimizer based on a new velocity updating and similarity detection mechanism. Lassad et al [15] designed a PSO procedure with new adaptive inertia weight and time acceleration coefficients for solving fuzzy clustering problems.…”

Section: Introductionmentioning

confidence: 99%

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

Wang

Liu

Sun

et al. 2020

Mathematical Problems in Engineering

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An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. e purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. is extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.

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A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization

Cited by 115 publications

References 33 publications

Improved Dynamic PSO-Based Algorithm for Critical Spare Parts Supply Optimization Under (T, S) Inventory Policy

Improved Dynamic PSO-Based Algorithm for Critical Spare Parts Supply Optimization Under (T, S) Inventory Policy

A PSO-based Algorithm for Reserve Price Optimization in Online Ad Auctions

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

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