S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization

Mirjalili, Seyedali; Lewis, Andrew

doi:10.1016/j.swevo.2012.09.002

Cited by 914 publications

(493 citation statements)

References 27 publications

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“…The search process is repeated until termination criterion is reached. The generation of a binary design solution of EDACE is, to some extent, similar to those used in binary PSO [49] and binary quantum-inspired gravitational search algorithm (BQIGSA) [52] in the sense that the binary solution is controlled by the probability of being "1" or "0". However, in EDACE, a generated solution relies not only on such probability but also on the reference binary solution b REF .…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Population-based incremental learning (PBIL) [24] used binary codes with the learning rate, mutation shift, and mutation rate as 0.5, 0.7, and 0.2, respectively. Binary particle swarm optimisation (BPSO) [49] used binary codes with V-shaped transfer function while the transfer function used is the V-shaped version 4 (V4) as reported in [49]. It is noted that this version is said to be the most efficient version based on the results obtained in [49].…”

Section: Flight Dynamic Control Optimisation Problemmentioning

confidence: 99%

“…Binary particle swarm optimisation (BPSO) [49] used binary codes with V-shaped transfer function while the transfer function used is the V-shaped version 4 (V4) as reported in [49]. It is noted that this version is said to be the most efficient version based on the results obtained in [49]. Univariate marginal distribution algorithm (UMDA) [48] used binary codes.…”

Section: Flight Dynamic Control Optimisation Problemmentioning

confidence: 99%

“…Apart from GA, other MHs using a binary string representing a design solution include a univariate marginal distribution algorithm (UMDA) [48], population-based incremental learning (PBIL) [24], binary particle swarm optimisation (BPSO) [49], binary simulated annealing (BSA) [50], binary artificial bee colony algorithm based on genetic operator (GBABC) [51], binary quantuminspired gravitational search algorithm (BQIGSA) [52], and self-adaptive binary variant of a differential evolution algorithm (SabDE) [53]. With the popularity of GA, a binarycode MH has been rarely developed and proposed while its real-code counterparts have over a hundred different search concepts reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Pholdee

Bureerat

2017

Mathematical Problems in Engineering

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A new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represent a binary population. Optimisation search is achieved by iteratively updating such a matrix. The performance assessment is conducted by comparing the new algorithm with existing binary-code metaheuristics including a genetic algorithm, a univariate marginal distribution algorithm, population-based incremental learning, binary particle swarm optimisation, and binary simulated annealing by using the test problems of CEC2015 competition and one real-world application which is an optimal flight control problem. The comparative results show that the new algorithm is competitive with other established binary-code metaheuristics.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Flight Dynamic Control Optimisation Problemmentioning

confidence: 99%

Section: Flight Dynamic Control Optimisation Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Pholdee

Bureerat

2017

Mathematical Problems in Engineering

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“…In single solution based type a single random solution is generated and improvised further while in multi solution based type multiple solutions are generated and modified. Usually multi solution based algorithms are chosen over single solution based algorithm [7,8].…”

Section: Introductionmentioning

confidence: 99%

Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Neve¹,

Kakandikar²,

Kulkarni³

2017

Int J Swarm Intel Evol Comput

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Charging cost minimisation by centralised controlled charging of electric vehicles

Patil

Kalkhambkar

2019

Int Trans Electr Energ Syst

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Summary With the recent breakthroughs in battery technology and large scale production, electric vehicles (EVs) are becoming cheaper. In a few years, mass deployment of EVs will put severe stress on the electricity network. Charging of EV during peak hours may overload the distribution grid transformer, and EV owners may have to pay more money for electricity during peak hours. To address these issues, a coordinated scheduling model is proposed in this paper. A mathematical model is formulated to minimise the charging cost of each EV while satisfying the constraints. In this work, time of use (ToU) tariff from the utility and actual power demand from household and EVs are used to conduct simulation for one week in summer and winter season with different levels of EV penetration. The results demonstrate that the proposed scheduling model can significantly reduce the charging cost for the EV owner and power peaks in the distribution network.

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S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization

Cited by 914 publications

References 27 publications

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Charging cost minimisation by centralised controlled charging of electric vehicles

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