Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018)

Deb, Sanchari; Xiao, Gao; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar

doi:10.1007/s10462-019-09718-3

Cited by 60 publications

(33 citation statements)

References 57 publications

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“…Statistical analyses on twelve benchmark problems demonstrate CSO dominance in terms of precision, robustness, and performance. There have also been several efforts to further boost CSO performance [13], [34], [35].…”

Section: B Ai-based Clustering Methodsmentioning

confidence: 99%

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CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

2020

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The rapid advancement in Wireless Sensor Network (WSN) technology has enabled smart environments to provide ubiquitous real-time applications in various fields such as industry, smart city, transport, health and Internet of Things (IoT). Energy is the most significant resource in WSNs as it has a direct effect on their lifetime. The efficient use of energy is required for the lifetime extension of WSNs. One of the well-known methods for achieving high scalability and efficient resource allocation in WSN is a clustering of sensor nodes. In this paper, the Chicken Swarm Optimization based Clustering Algorithm (CSOCA) is proposed to improve energy efficiency in WSNs. The chicken swarm optimization is discretized by applying a sigmoid function to individuals. Moreover, we proposed CSOCA with Genetic Algorithm (CSOCA-GA) which is an improvement to CSOCA by employing the Genetic Algorithm's processes in CSOCA. CSOCA-GA utilizes crossover and mutation processes for individuals with low fitness value to extend the population diversity. CSOCA and CSOCA-GA are tested and compared with other similar algorithms to confirm their effectiveness in terms of extending WSN lifetime and reducing energy consumption. INDEX TERMS Wireless sensor network, clustering, Internet of Things, genetic algorithm, chicken swarm optimization.

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Section: B Ai-based Clustering Methodsmentioning

confidence: 99%

“…Chicken Swarm Optimization (CSO) algorithm is a bio-inspired algorithm [12], [13], which simulated foraging activities of the chicken swarm by divided chickens into different subgroups. In each subgroup, every individual iterated simultaneously towards the optimal one.…”

Section: Introductionmentioning

confidence: 99%

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

2020

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“…CSO mimics the behaviour of the chicken swarm and the food searching procedure of the swarm [22], [45]. The group is divided into the dominant rooster, hens, and chicks on the The random assignment of the mother-child relationship in the swarm is also a salient feature of the algorithm.…”

Section: A Multi-objective Csomentioning

confidence: 99%

“…The mother hen selection is made randomly. The algorithm assumes that the number of chicks is smaller than that of hens, and hens are the largest in the group [22]. There aresome differences in the food searching process of roosters, hens, and chicks.…”

Section: A Multi-objective Csomentioning

confidence: 99%

“…CSO and TLBO are the two state-of-the-art nature-inspired algorithms successfully applied by researchers for complex engineering optimization problems. In [22], the authors have reviewed different variants, applications of CSO as well as efficacy of CSO in solving different real-world problems. For example, CSO is applied for solving economic load dispatch [23], fault diagnosis of pumping wells [24], ascent trajectory optimization [25], train energy saving [26], robot path planning [27] etc.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem

et al. 2020

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A new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electric power grid is not overloaded. The optimization framework aims at simultaneously reducing the cost, guaranteeing sufficient grid stability and feasible charging station accessibility. The grid stability is measured by a composite index consisting of Voltage stability, Reliability, and Power loss (VRP index). A Pareto dominance based hybrid Chicken Swarm Optimization and Teaching Learning Based Optimization (CSO TLBO) algorithm is utilized to obtain the Pareto optimal solution. It amalgamates swarm intelligence with teachinglearning process and inherits the strengths of CSO and TLBO. The two level algorithm has been validated on the multi-objective benchmark problems as well as EV charging station placement. The performance of the Pareto dominance based CSO TLBO is compared with that of other state-of-the-art algorithms. Furthermore, a fuzzy decision making is used to extract the best solution from the non dominated set of solutions. The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem. INDEX TERMS Accessibility index, charging station, chicken swarm optimization, teaching learning optimization, cost, electric vehicle.

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An improved chicken swarm optimization algorithm for extracting the optimal parameters of proton exchange membrane fuel cells

Ayvaz

2022

Intl J of Energy Research

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Summary In this study, an improved variant of chicken swarm optimization (CSO), named I‐CSO, is proposed to find the unknown parameters of the proton exchange membrane fuel cell (PEMFC) models. Although the basic CSO has a well‐established population hierarchy mechanism that gives it an important advantage over its competitors, it suffers from premature convergence and can be easily trapped into the local optima because of inadequate use of population information in the update rule of the rooster's position. In the proposed I‐CSO, this shortcoming is addressed by introducing a new learning strategy for the roosters, which play leadership roles in the foraging behavior of the chicken swarm, to improve the algorithm convergence capability. Moreover, an adaptive inertia weight is introduced to make the algorithm more stable by striking a better balance between the exploration and exploitation phase. The sum of absolute error between the actual and estimated voltage outputs of the stack is suggested as the objective function to perform the optimization. Besides the suggested one, two other objective functions are also used to evaluate the impact of objective function choice on the optimization results. The test of the method is performed on two commercial PEMFCs, which are BCS 500‐W Stack and NedStack PS6, and the results of I‐CSO are compared with those of other competitive algorithms published in the literature. The final results show that the use of the proposed I‐CSO with the suggested objective function demonstrates excellent performance in estimating the PEMFC model parameters with fewer errors.

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Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018)

Cited by 60 publications

References 57 publications

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem

An improved chicken swarm optimization algorithm for extracting the optimal parameters of proton exchange membrane fuel cells

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