A mechanism based on Artificial Bee Colony to generate diversity in Particle Swarm Optimization

Vitorino, L. N.; Ribeiro, Sérgio; Bastos-Filho, Carmelo J. A.

doi:10.1016/j.neucom.2013.03.076

Cited by 37 publications

(18 citation statements)

References 14 publications

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“…Information flow, between particle swarm and bee colony, helped increase global and local search abilities of the hybrid approach. Vitorino et al [144] put forward a mechanism based on the ABC to generate diversity when all particles of the PSO converged to a single point of the search space. Then, the swarm entities switched between two predefined behaviors by using fuzzy rules depending on the diversity of the whole swarm.…”

Section: With Aco Chen and Chienmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

690

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 Aco Chen and Chienmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

690

380

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“…To obtain error-free communication and also to increase the network's lifetime, swarm intelligence (artificial bee colony) [18][19][20][21][22][23][24] is used in the trustworthy energy-efficient MIMO routing protocol. The proposed ABC-based trustworthy energy-efficient routing system is shown in Figure 5.…”

Section: Abc-based Trustworthy Energy-efficient Mimo Routing Protocolmentioning

confidence: 99%

ABC algorithm‐based trustworthy energy‐efficient MIMO routing protocol

Sathian

2019

Int J Communication

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Summary This paper introduces artificial bee algorithm (ABC)‐based energy‐efficient protocol with security for the enhancement of lifetime of wireless sensor network. A trustworthy energy‐efficient routing MIMO (TEEM) technique is used by implementing MIMO technique in TEER protocol to isolate malicious nodes and to reduce the fading effects and interference in the network. The performance of ABC‐based trustworthy energy‐efficient MIMO routing (ABC‐TEEM) protocol is analysed. Alive node performance, residual energy, throughput analysis, and pocket loss rate of ABC‐TEEM protocol for WSN are computed and compared with the performance of existing TEEM protocol for various diversity orders.

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“…Vitorino, et al [134] 2015 ABeePSO Optimization problems Lin and Hsieh [135] 2015 EPSO_ABC Classification of Medical Datasets Using SVMs Zhou and Yang [136] 2015 PSO-DE-PABC and PSO-DE-GABC…”

Section: Hybridization Of Pso Using Artificial Bee Colony (Abc)mentioning

confidence: 99%

Particle Swarm Optimization: A survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta¹,

Basak²,

Peters³

2018

Preprint

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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state–of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

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A mechanism based on Artificial Bee Colony to generate diversity in Particle Swarm Optimization

Cited by 37 publications

References 14 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

ABC algorithm‐based trustworthy energy‐efficient MIMO routing protocol

Particle Swarm Optimization: A survey of Historical and Recent Developments with Hybridization Perspectives

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