A Hybrid CS/GA Algorithm for Global Optimization

Ghodrati, Amirhossein; Lotfi, Shahriar

doi:10.1007/978-81-322-0487-9_38

Cited by 15 publications

(6 citation statements)

References 9 publications

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“…The technique has been tested on 10 benchmark functions and 1 nonlinear design problem. The results were better than PSO and GA methods A hybrid CS/PSO algorithm for global optimization [ 97 ] Ghodrati et al 2012 The authors in this paper presented hybrid CS/PSO method to crack optimization issues. The technique has been examined on many benchmark functions to prove better than other techniques Biogeography-based optimization (BBO) [ 27 ] Simon 2008 14 typical benchmark functions were used in the testing.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems

Kumari

Kamboj

Bath

2022

Complex Intell. Syst.

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The existing slime mould algorithm clones the uniqueness of the phase of oscillation of slime mould conduct and exhibits slow convergence in local search space due to poor exploitation phase. This research work exhibits to discover the best solution for objective function by commingling slime mould algorithm and simulated annealing algorithm for better variation of parameters and named as hybridized slime mould algorithm–simulated annealing algorithm. The simulated annealing algorithm improves and accelerates the effectiveness of slime mould technique as well as assists to take off from the local optimum. To corroborate the worth and usefulness of the introduced strategy, nonconvex, nonlinear, and typical engineering design difficulties were analyzed for standard benchmarks and interdisciplinary engineering design concerns. The proposed technique version is used to evaluate six, five, five unimodal, multimodal and fixed-dimension benchmark functions, respectively, also including 11 kinds of interdisciplinary engineering design difficulties. The technique’s outcomes were compared to the results of other on-hand optimization methods, and the experimental results show that the suggested approach outperforms the other optimization techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems

Kumari

Kamboj

Bath

2022

Complex Intell. Syst.

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“…Cuckoo Search (CS) proposed by Xin-She Yang and Suash Deb [119] was developed on the basis of breeding behavior of cuckoos associated with the levy flight nature of birds and flies. Subsequently, Ghodrati and Lotfi [120] introduced Hybrid CS/PSO Algorithm capturing the ability of the cuckoos to communicate with each other in order to decrease the chances of their eggs being identified and abandoned by the host birds, by using Particle Swarm Optimization (PSO). In the course of migration each cuckoo records its personal best, thus generating the global best and governing their movements accordingly.…”

Section: Table 2 a Collection Of Hybridized De-pso Algorithmsmentioning

confidence: 99%

“…Author/s: Year Algorithm Area of Application Ghodrati and Lotfi [120] 2012 Hybrid CS/PSO Global optimization Nawi et al [121] 2014 HACPSO Classification Enireddy and Kumar [122] 2015…”

Section: Table 5 a Collection Of Hybridized Pso-cs Algorithmsmentioning

confidence: 99%

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

Sengupta

Basak

Peters

2018

MAKE

387

184

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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 cooperation 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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“…In the literature (see, e.g., [ 17 ]), it is possible to find PSO-based hybrid algorithms that use, for example, Ant Colony Optimisation (ACO) (e.g., [ 133 ]), a population based meta-heuristic algorithm inspired by the social behaviour of real-life ants searching for food; Cuckoo Search [ 134 ], a meta-heuristic approach idealised to reproduce the breeding behaviour of cuckoo birds, who leave their eggs in the nests of other host birds of different species; and Artificial Bee Colony (ABC) optimisation [ 135 ], a swarm-based meta-heuristic algorithm based on the behaviour of real honey bee colonies, which are organised in groups of bees to maximise the nectar amount found in a food source.…”

Section: Connections To Other Artificial Intelligence Toolsmentioning

confidence: 99%

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

Freitas

Lopes

Morgado‐Dias

2020

Entropy

153

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The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvements is made, which also covers the hybridisation and parallelisation of the algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones. These approaches and improvements are appropriately summarised, organised and presented, in order to allow and facilitate the identification of the most appropriate PSO variant for a particular application.

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A Hybrid CS/GA Algorithm for Global Optimization

Cited by 15 publications

References 9 publications

Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems

Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems

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

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

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