The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization

Elloumi, Walid; Baklouti, Nesrine; Abraham, Ajith; Alimi, Adel M.

doi:10.3233/ifs-131020

Cited by 31 publications

(9 citation statements)

References 47 publications

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“…The hybridization of the two algorithms to optimize the forecasting model led to a higher quality result with a faster convergence profile. Elloumi et al [133] illustrated a novel optimization approach based on multiobjective PSO (MOPSO) and Fuzzy ACO (FACO). The basic idea was to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local PSO to formulate a new approach called hybrid MOPSO with FACO (H-MOPSO-FACO).…”

Section: With Tsmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

697

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: With Tsmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

697

380

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“…The two algorithms' hybridization to optimize the expected model led to an improved output outcome with a high convergence rate. The new approach to optimization based on the multi-objective PSO and Fuzzy ACO was illustrated by Elloume et al [52]. These two strategies must be paired with the highest particle in the Fuzzy Ant algorithm to form a new system called hybrid MOPSO with FACO as the best local PSO.…”

Section: Pso-aco Hybrid Algorithmmentioning

confidence: 99%

Evolutionary Algorithms for Real Time Engineering Problems: A Comprehensive Review

Kumar¹,

Ganesh²,

Bhoopal³

et al. 2021

ISI

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This paper presents a variety of contemporary optimization techniques inspired by the real life in nature. Optimization reveals substantial developments in computing systems as well as has come to be the most encouraging strategy for several design applications. The study is conducted on single-objective, multi-objective, and hybrid optimization strategies. These optimization schemes will be of excellent help to organizations to identify optimum criteria and to improve process as well as product high quality. For selected optimization strategies, the process of formulating the objective function/stiffness function for a minimal issue exists. Over the last few years, the most combinatoric problems of all traditional optimization approaches were solved by using metaheuristic algorithms to have optimal solutions for real-time applications. This paper discussed some of the important and feasible optimization scheme and the related algorithms and approaches.

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“…An ensemble of particles flying in a precise search space constitutes what we call the swarm or the population. The particles' nature is strongly dependent on the problem to be studied [26]. There is a pair composed of position and velocity (pi, vi) assigned to every particle in the swarm.…”

Section: Single and Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations

et al. 2019

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The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization

Cited by 31 publications

References 47 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Evolutionary Algorithms for Real Time Engineering Problems: A Comprehensive Review

Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations

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