A Comprehensive Study of Particle Swarm Based Multi-objective Optimization

Mohankrishna, Samantula; Maheshwari, D.; Satyanarayana, P.; Satapathy, Suresh Chandra

doi:10.1007/978-3-642-27443-5_79

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…After these two pioneering works, a variety of PSO algorithms for handling multi-objective optimization problems have been developed in the last few decades [24]. Different from the PSO for single objective problems, the goal of multi-objective PSO (MOPSO) algorithm is to obtain a good representative of the entire PF of the target MOP.…”

Section: The Multi-objective Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

Luo

Xie

et al. 2015

Applied Soft Computing

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Section: The Multi-objective Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…It is demonstrated that PSO A c c e p t e d M a n u s c r i p t 5 gets better results in a faster, cheaper way compared with other methods. Recently, there has been a growing interest in multi-objective particle swarm optimization (MOPSO) which investigates PSO techniques for handling MOPs [24].…”

Section: Introductionmentioning

confidence: 99%

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

Luo

Xie

et al. 2015

Applied Soft Computing

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“…Both GAs and PSOs are designed for single objective optimization. In order to solve multiobjective optimization, representative algorithms like the non-dominated sorting genetic algorithm-version II (NSGA-II) [34], multiobjective particle swarm optimization (MOPSO) [35], multiobjective evolutionary algorithm based on decomposition (MOEA/D) [36], etc., have been developed by scientists and been applied to solve many engineering problems.…”

Section: Algorithm Frameworkmentioning

confidence: 99%

A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement

Liu²,

Chen

2021

Front. Phys.

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A complex network can crash down due to disturbances which significantly reduce the network’s robustness. It is of great significance to study on how to improve the robustness of complex networks. In the literature, the network rewire mechanism is one of the most widely adopted methods to improve the robustness of a given network. Existing network rewire mechanism improves the robustness of a given network by re-connecting its nodes but keeping the total number of edges or by adding more edges to the given network. In this work we propose a novel yet efficient network rewire mechanism which is based on multiobjective optimization. The proposed rewire mechanism simultaneously optimizes two objective functions, i.e., maximizing network robustness and minimizing edge rewire operations. We further develop a multiobjective discrete partite swarm optimization algorithm to solve the proposed mechanism. Compared to existing network rewire mechanisms, the developed mechanism has two advantages. First, the proposed mechanism does not require specific constraints on the rewire mechanism to the studied network, which makes it more feasible for applications. Second, the proposed mechanism can suggest a set of network rewire choices each of which can improve the robustness of a given network, which makes it be more helpful for decision makings. To validate the effectiveness of the proposed mechanism, we carry out experiments on computer-generated Erdős–Rényi and scale-free networks, as well as real-world complex networks. The results demonstrate that for each tested network, the proposed multiobjective optimization based edge rewire mechanism can recommend a set of edge rewire solutions to improve its robustness.

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“…They were made the first comparisons between MOPSO with multi-objective evolutionary algorithms. After these works, a many PSO algorithms types to handle MOO problems have been developed [32]. Literature [33] used roulette wheel selection to select the global best particle from the trade-off solutions.…”

Section: Related Workmentioning

confidence: 99%

A Particle Swarm Optimization Based on Multi Objective Functions with Uniform Design

Al-Mter¹,

Lu²,

Al-Salhi³

et al. 2016

IJCSE

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A Multi-objective problems occurs wherever optimal solution necessary to be taken in the presence of tradeoffs between more than one conflicting objectives. Usually the population's values of MOPSO algorithm are random which leads to random search quality. Particle Swarm Optimization Based on Multi Objective Functions with Uniform Design (MOPSO-UD), is proposed to enhance the accuracy of the particles convergence and keep the versatility of the Pareto optimal solutions and used the Uniform design to resolve the randomize search problem of the original MOPSO algorithm also the execution time of MOPSO-UD is faster compared with multi-objective particle swarm optimization algorithm (MOPSO).

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A Comprehensive Study of Particle Swarm Based Multi-objective Optimization

Cited by 5 publications

References 33 publications

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement

A Particle Swarm Optimization Based on Multi Objective Functions with Uniform Design

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