Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

Wang, Jiquan; Cheng, Zhiwen; Ersoy, Okan K.; Zhang, Mingxin; Sun, Kexin; Bi, Yusheng

doi:10.1109/access.2019.2913180

Cited by 19 publications

(12 citation statements)

References 27 publications

(72 reference statements)

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“…In the basic CSO algorithm, the position update of a rooster is of great randomness, which has a strong influence on the convergence speed of the CSO algorithm. CSO is easy to fall into a local optimum in the late iterations [15]. In this paper, we adopt the cosine function to adjust a rooster position, and the periodic variation of the cosine function will affect the updating step length of a rooster's position in the later iteration stage, which can help CSO jump out of the local optimal solutions.…”

Section: E Fcso Algorithmmentioning

confidence: 99%

“…In this section, we apply the proposed FCSO algorithm to four types of constrained EOPs to further evaluate the performance of FCSO. ese engineering optimization problems are often used to test the performance of SIOAs [11,15]. In the next, we first introduce these four engineering designs and their constraints and then present the experiment results of applying the CSO and FCSO to these problems and make a brief comparison of their performance.…”

Section: Algorithmsmentioning

confidence: 99%

“…Fu et al [14] modified the update equation of roosters and introduced a mutation operator to solve the problem of easily falling into a trap of local optimal solutions. Furthermore, the CSO algorithm has also been extended to solve the constraint optimization problem [15] (see Section 6.1), 0-1 knapsack problem [16], and multiobjective optimization problem [17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Wang

Qin

Wan

et al. 2021

Mathematical Problems in Engineering

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The chicken swarm optimization (CSO) algorithm is a new swarm intelligence optimization (SIO) algorithm and has been widely used in many engineering domains. However, there are two apparent problems with the CSO algorithm, i.e., slow convergence speed and difficult to achieve global optimal solutions. Aiming at attacking these two problems of CSO, in this paper, we propose an adaptive fuzzy chicken swarm optimization (FCSO) algorithm. The proposed FCSO uses the fuzzy system to adaptively adjust the number of chickens and random factors of the CSO algorithm and achieves an optimal balance of exploitation and exploration capabilities of the algorithm. We integrate the cosine function into the FCSO to compute the position update of roosters and improve the convergence speed. We compare the FCSO with eight commonly used, state-of-the-art SIO algorithms in terms of performance in both low- and high-dimensional spaces. We also verify the FCSO algorithm with the nonparametric statistical Friedman test. The results of the experiments on the 30 black-box optimization benchmarking (BBOB) functions demonstrate that our FCSO outperforms the other SIO algorithms in both convergence speed and optimization accuracy. In order to further test the applicability of the FCSO algorithm, we apply it to four typical engineering problems with constraints on the optimization processes. The results show that the FCSO achieves better optimization accuracy over the standard CSO algorithm.

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Section: E Fcso Algorithmmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Wang

Qin

Wan

et al. 2021

Mathematical Problems in Engineering

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“…In [21], an improved CSO algorithm is proposed for analysing optimization capabilities on various benchmark functions in high dimensions. As investigated in [22] the designed CSO algorithm is capable of solving constrained based optimization problems with fast convergence rate and high precision in both low and high dimensions. In [23] CSO algorithm is used with CO-OFDM system to achieve an optimal phase solution for the PAPR reduction of OFDM signals.…”

Section: Introductionmentioning

confidence: 99%

Chicken Swarm Optimization for PTS based PAPR Reduction in OFDM Systems

Gupta

Goel

2020

J. commun. softw. syst. (Online)

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Partial transmit sequence (PTS) is a well-known PAPR reduction scheme for the OFDM system. One of the major challenge of this scheme is to find an optimal phase vector using exhaustive search over all the allowed phase factor combinations. This leads to increased search complexity which grows exponentially as the number of sub-blocks is increased. In this paper, chicken swarm optimization (CSO) based PTS system is designed that aims to find an optimal solution in less number of average iterations and therefore results in reduced computational complexity of the system. We have proposed two categories of the algorithm: (i) CSO-PTS system without threshold limit on PAPR (ii) CSO-PTS system with threshold limit on PAPR. Both the schemes offer effective trade-offs between the computationalcomplexity and the PAPR reduction capability of the system. Simulation results confirm that our proposed schemes perform well in terms of low computational complexity, lesser number of average iterations and improved PAPR reduction capability of the OFDM signal without any loss in BER performance of the system.

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“…Deb [16] gave a review of recent work on CSO from the perspective of improvement strategy and application. Wang [17] improved the updating law of roosters by the center of all the hens and applied the uniform mutation to escape the local minimum. Li [18] came up with a stochastic gradient PSO for the hypersonic vehicle reentry phase, which used the history optimal solutions to accelerate the search.…”

Section: Introductionmentioning

confidence: 99%

Ascent Trajectory Optimization for Hypersonic Vehicle Based on Improved Chicken Swarm Optimization

Wang

et al. 2019

IEEE Access

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Trajectory optimization problem for hypersonic vehicles has received wide attention as its high speed and large flight range. The strong nonlinear characteristic of the ascent phase aerodynamics makes the trajectory optimization problem difficult to be solved by the optimal control theory. In this paper, an improved chicken swarm optimization (ICSO) algorithm is proposed to optimize the hypersonic vehicle ascent trajectory. To overcome the obstacle of premature convergence, three improvement strategies are put forward. To be specific, the updating laws of roosters are modified by the average position of roosters, and the difference of the optimal solution between two adjacent iterations is used to calculate the mutated particle instead of the gradient. Meanwhile, the uniform mutation operator is used to get rid of the local minimum. The convergence analysis of the proposed ICSO is provided subsequently. To handle constraints, an improved adaptive penalty method is put forward. The comparison results show that the proposed ICSO outperforms CSO and PSO on benchmark functions of CEC2014. Finally, the trajectory optimization results for a generic hypersonic vehicle, in compare with the open-source optimization software PSOPT, are put forward to demonstrate the feasibility and effectiveness of the proposed method. The results of 50 independent runs show that the improved adaptive penalty function method is effective in constraints handling.

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Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

Cited by 19 publications

References 27 publications

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Chicken Swarm Optimization for PTS based PAPR Reduction in OFDM Systems

Ascent Trajectory Optimization for Hypersonic Vehicle Based on Improved Chicken Swarm Optimization

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