Mutation chicken swarm optimization based on nonlinear inertia weight

Wang, Kang; Zhen-bi, LI; Cheng, Hao; Zhang, Kun

doi:10.1109/compcomm.2017.8322928

Cited by 11 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Definition 2: The sum of the number of times that satisfies equation (22) or both equations ( 22) and ( 23) is called the total number of successes, abbreviated as sucnumber.…”

Section: B Algorithm Performance Evaluation Indexmentioning

confidence: 99%

“…In recent years, CSO has attracted people's attention with its unique and excellent performance, and many scholars have conducted in-depth research on CSO. Research on CSO algorithms can be grouped into the following categories: (1) improvement of rooster position update method [18]- [22]; (2) improvement of hen position update method [18], [20], [23], [24]; location update method [6], [9], [12], [20], [22], [8], [17], [25]- [27]; (4) adjustment of algorithm parameters [18], [25]; (5) introducing other algorithmic operators [19], [24], [28]- [31]; (6) convergence analysis of algorithm [25]. The progress with these algorithms has improved the computational speed and promoted the development of CSO theory with new applications.…”

Section: Introductionmentioning

confidence: 99%

“…The simulation results showed that the improved chicken swarm optimization has notable improvement in the optimal solution, the worst solution, the average solution and the variance of the solution. In 2017, Wang et al [22] proposed an improved chicken swarm optimization, which gives a rooster position update method with nonlinear dynamic decrement weights and a chick with Gaussian mutation operation. The location update method makes the results of the solution significantly improved in the optimal solution, the worst solution and the average solution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

et al. 2019

View full text Add to dashboard Cite

Aiming at the problem of slow convergence speed and ease of falling into local optimum when solving high dimensional problems, this paper proposes an improved chicken swarm optimization algorithm. The improved chicken swarm optimization includes four aspects, namely, cock position update mode, hen position update mode, chick position update mode, and population update strategy, so it is abbreviated as ICSO-RHC. On the basis of algorithm improvement, the influence of the number of retained elite individuals and control parameters on the convergence speed of the algorithm is discussed. The calculation results of the test function show that when the number of elite individuals in the population is 1, and the control parameters is a random number uniformly distributed between [0, 1], the algorithm has a faster convergence speed. In addition, in order to verify the performance of ICSO-RHC, 30 test functions and CEC 2005 benchmark functions were selected. The calculation results of these test functions show that the success rate of ICSO-RHC is significantly higher than other algorithms, both for low-dimensional and highdimensional optimization problems. The average iteration number and average running time are significantly lower than other algorithms. Finally, ICSO-RHC and other improved algorithms in the literature are used to optimize the parameters of four practical engineering problems. The optimization results show that the statistical results obtained by ICSO-RHC are significantly better than other algorithms. The calculation results of the test functions and the actual engineering problems show that the performance of ICSO-RHC proposed in this paper is significantly better than other algorithms.INDEX TERMS Constrained optimization problem, chicken swarm optimization, rooster position update, hen position update, chick position update.

show abstract

“…Definition 2: The sum of the number of times that satisfies equation (22) or both equations ( 22) and ( 23) is called the total number of successes, abbreviated as sucnumber.…”

Section: B Algorithm Performance Evaluation Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Chen et al (2015) proposed an improved version of CSO with modified update equation of the hen. Wang et al (2017) introduced the mutation strategy in the update phenomenon of chicks to enhance their food searching ability. Han et al (2017) also proposed an improved binary version of CSO where the mutation operator is applied to the population with the worst fitness value.…”

Section: Introductionmentioning

confidence: 99%

“…However, the algorithm may sometimes get trapped in local optima. Researchers have tried to overcome this inherent drawback of CSO by variety of ways (Chen et al 2015;Wang et al 2017;Han et al 2017;Liang et al 2016;Kumar &Veni 2018;Torabi & Esfahani 2018 ). Some of the variants of CSO are listed in Table 1.The present work also makes an attempt to improve the basic CSO by modifying the update equation of roosters and introducing a novel constraint handling mechanism.…”

Section: Introductionmentioning

confidence: 99%

A New Teaching–Learning-based Chicken Swarm Optimization Algorithm

et al. 2019

View full text Add to dashboard Cite

Chicken Swarm Optimization (CSO) is a novel swarm intelligence based algorithm known for its good performance on many benchmark functions as well as real world optimization problems. However, it is observed that CSO sometimes gets trapped in local optima. This work proposes an improved version of the CSO algorithm with modified update equation of the roosters and a novel constraint handling mechanism. Further, the work also proposes synergy of the improved version of CSO (ICSO) with Teaching Learning Based Optimization (TLBO) algorithm. The proposed ICSOTLBO algorithm possesses the strengths of both CSO and TLBO. The efficacy of the proposed algorithm is tested on eight basic benchmark functions, fifteen computationally expensive benchmark functions as well as two real-world problems. Further, the performance of ICSOTLBO is also compared with a number of state of art algorithms. It is observed that the proposed algorithm performs better than or as good as many of the existing algorithms.

show abstract

Chicken swarm optimization with an enhanced exploration–exploitation tradeoff and its application

et al. 2023

View full text Add to dashboard Cite

The chicken swarm optimization (CSO) is a novel swarm intelligence algorithm, which mimics the hierarchal order and foraging behavior in the chicken swarm. However, like other population-based algorithms, CSO also suffers from slow convergence and easily falls into local optima, which partly results from the unbalance between exploration and exploitation. To tackle this problem, this paper proposes a chicken swarm optimization with an enhanced exploration-exploitation tradeoff (CSO-EET). To be specific, the search process in CSO-EET is divided into two stages (i.e., exploration and exploitation) according to the swarm diversity. In the exploratory search process, a random solution is employed to find promising solutions. In the exploitative search process, the best solution is used to accelerate convergence. Guided by the swarm diversity, CSO-EET alternates between exploration and exploitation. To evaluate the optimization performance of CSO-EET in both theoretical and practical problems, it is compared with other improved CSO variants and several state-of-the-art algorithms on twenty-two benchmark functions and two real-world problems (i.e., circle packing problem and survival risk prediction of esophageal cancer). The experimental results show that CSO-EET is better than or at least com-parable to all competitors in most cases.

show abstract

Mutation chicken swarm optimization based on nonlinear inertia weight

Cited by 11 publications

References 6 publications

Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

A New Teaching–Learning-based Chicken Swarm Optimization Algorithm

Chicken swarm optimization with an enhanced exploration–exploitation tradeoff and its application

Contact Info

Product

Resources

About