A scalable parallel cooperative coevolutionary PSO algorithm for multi-objective optimization

Atashpendar, Arash; Dorronsoro, Bernabè; Danoy, Grégoire; Bouvry, Pascal

doi:10.1016/j.jpdc.2017.05.018

Cited by 41 publications

(11 citation statements)

References 12 publications

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“…An even brother conclusion would be that all swarm intelligence algorithms could benefit from the parallelization. This has been demonstrated by (Dao et al, 2018) who have applied parallelization on bat algorithm and by (Atashpendar et al, 2018) and (Gülcü & Kodaz, 2015) who have demonstrated the parallelization of PSO algorithm. Additionally, some researches have demonstrated the reduction of the execution time like the ones conducted by (Thiruvady et al, 2016) and (Ouyang et al, 2015).…”

Section: Resultsmentioning

confidence: 94%

Parallelized Multiple Swarm Artificial Bee Colony (PMS-ABC) Algorithm for Constrained Optimization Problems

Subotic¹,

Manasijevic²,

Kupusinac³

2020

STUD INFORM CONTROL

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Since their introduction, bio-inspired algorithms, especially the ones based on the social behaviour of the animals that live in colonies have demonstrated great potential in finding near-optimal solutions for both unconstrained and constrained hard optimization problems. In this research, a parallel version of the popular Artificial Bee Colony (ABC) algorithm for optimization of constrained problems, has been introduced. An island-based model, in which the whole population is divided into subpopulations, is used. Subpopulations execute the serial version of the original algorithm and occasionally exchange the obtained results. The proposed algorithm has been tested based on a set of well-known constraint benchmark functions and five real-world engineering design problems. The results demonstrate clear improvements compared with those obtained with the original ABC algorithm.

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Section: Resultsmentioning

confidence: 94%

Parallelized Multiple Swarm Artificial Bee Colony (PMS-ABC) Algorithm for Constrained Optimization Problems

Subotic¹,

Manasijevic²,

Kupusinac³

2020

STUD INFORM CONTROL

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“…As one of the most popular metaheuristics for solving optimization problems, particle swarm optimization (PSO) is a kind of swarm intelligence paradigm originally inspired by the behavior of bird flocking in nature [30]. Since the first multi-objective PSO algorithm was proposed [31], a number of PSO algorithms have been proposed for solving MOPs [32]- [42]. The most significant characteristic of PSO is that each particle (i.e., solution) has a position vector denoting the decision variables and a velocity vector, where the position is updated according to the velocity, and the velocity is updated according to the personal best position and the global best position.…”

Section: A Existing Particle Updating Strategies For Solving Mopsmentioning

confidence: 99%

“…For instance, in CVEPSO [41], the vector-evaluated PSO [43] is equipped with cooperative principles [44], which successfully competes with state-of-the-art multi-objective PSO algorithms. In CCSMPSO [42], the SMPSO [33] is enhanced and parallelized by a cooperative coevolutionary paradigm, where both the effectiveness and efficiency are improved.…”

Section: A Existing Particle Updating Strategies For Solving Mopsmentioning

confidence: 99%

Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer

Tian

Zheng

Zhang

et al. 2020

IEEE Trans. Cybern.

287

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There exist many multi-objective optimization problems (MOPs) containing a large number of decision variables in real-world applications, which are known as large-scale MOPs. Due to the ineffectiveness of existing operators in finding optimal solutions in a huge decision space, some decision variable division based algorithms have been tailored for improving the search efficiency in solving large-scale MOPs. However, these algorithms will encounter difficulties when solving problems with complicated landscapes, as the decision variable division is likely to be inaccurate and time-consuming. In this paper, we propose a competitive swarm optimizer (CSO) based efficient search for solving large-scale MOPs. The proposed algorithm adopts a new particle updating strategy that suggests a twostage strategy to update position, which can highly improve the search efficiency. Experimental results on large-scale benchmark MOPs and an application example demonstrate the superiority of the proposed algorithm over several stateof-the-art multi-objective evolutionary algorithms, including problem transformation based algorithm, decision variable clustering based algorithm, particle swarm optimization algorithm, and estimation of distribution algorithm. Index Terms-Evolutionary multi-objective optimization, large-scale multi-objective optimization problem, competitive swarm optimizer, particle swarm optimization I. INTRODUCTION M ULTI-objective optimization problems (MOPs) are commonly seen in real-world applications [1]-[5], which are characterized by multiple objectives conflicting with each other. Due to the conflicting nature of the objectives, a set of trade-off solutions rather than a single Manuscript received- .

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“…where formulas (16) and (17) are used to update both the velocity and position for pickup services and formulas (18) and (19) are used to update both the velocity and position for delivery services. 1 and 2 are two acceleration coefficients.…”

Section: Ga Updates and The Corresponding Pso Operationsmentioning

confidence: 99%

“…The proposed algorithm was applied and proved many times faster than GAMS. Atashpendar et al [18] extended the speed-constrained multiobjective PSO algorithm to increase computation speed, convergence, and solution quality.…”

Section: Introductionmentioning

confidence: 99%

Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

Wang

Yuan

Assogba

et al. 2018

Journal of Advanced Transportation

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In modern supply chain, logistics companies usually operate individually and optimization researches often concentrate on solving problems related to separate networks. Consequences like the complexity of urban transportation networks and long distance deliveries or pickups and pollution are leading problems to more expenses and more complaints from environment protection organizations. A solution approach to these issues is proposed in this article and consists in the adoption of two-echelon heterogeneous cooperative logistics networks (THCLN). The optimization methodology includes the formation of cooperative coalitions, the reallocation of customers to appropriate logistics facilities, and the determination of the best profit allocation scheme. First, a mixed integer linear programing model is introduced to minimize the total operating cost of nonempty coalitions. Thus, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are hybridized to propose GA-PSO heuristics. GA-PSO is employed to provide good solutions to customer clustering units' reallocation problem. In addition, a negotiation process is established based on logistics centers as coordinators. The case study of Chongqing city is conducted to verify the feasibility of THCLN in practice. The grand coalition and two heterogeneous subcoalitions are designed, and the collective profit is distributed based on cooperative game theory. The Minimum Cost Remaining Savings (MCRS) model is used to determine good allocation schemes and strictly monotonic path principles are considered to evaluate and decide the most appropriate coalition sequence. Comparisons proved the combination of GA-PSO and MCRS better as results are found closest to the core center. Therefore, the proposed approach can be implemented in real world environment, increase the reliability of urban logistics network, and allow decision makers to improve service efficiency.

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A scalable parallel cooperative coevolutionary PSO algorithm for multi-objective optimization

Cited by 41 publications

References 12 publications

Parallelized Multiple Swarm Artificial Bee Colony (PMS-ABC) Algorithm for Constrained Optimization Problems

Parallelized Multiple Swarm Artificial Bee Colony (PMS-ABC) Algorithm for Constrained Optimization Problems

Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer

Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

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