Convergence Analysis and Improvement of the Chicken Swarm Optimization Algorithm

Wu, Dinghui; Xu, Shipeng; Kong, Fei

doi:10.1109/access.2016.2604738

Cited by 75 publications

(60 citation statements)

References 29 publications

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“…These models could stimulate computer scientists using household nontraditional tools to solve the application problems [3]. Now a lot of swarm intelligence optimization algorithms are proposed, such as particle swarm optimization (PSO) [4], ant colony algorithm (ACO) [5], bat algorithm (BA) [6], social learning optimization (SLO) algorithm [7], and chicken swarm optimization (CSO) algorithm [8]. They can be used in the dictionary learning remote sensing data, automotive safety integrity level positioning, economic dispatch, composition, and examples of the Cloud Service Composition of QOS awareness.…”

Section: Introductionmentioning

confidence: 99%

Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

Wang

2018

Journal of Electrical and Computer Engineering

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The bat algorithm (BA) is a new bionic intelligent optimization algorithm to simulate the foraging behavior and the echolocation principle of the bats. The parameter initialization of the discussed binary bat algorithm (BBA) has important influence on the convergence speed, convergence precision, and good global searching ability of the BBA. The convergence speed and algorithm searching precision are determined by the pulse of loudness and pulse rate. The simulation experiments are carried out by using the six typical test functions to discuss this influence. The simulation results show that the convergence speed of the BBA is relatively sensitive to the setting of the algorithm parameters. The convergence precision reduces when increasing the rate of bat transmitted pulse alone and the convergence speed increases the launch loudness alone. The proper combination of BBA parameters (the rate of bat transmitted pulse and the launch loudness) can flexibly improve the algorithm's convergence velocity and improve the accuracy of the searched solutions.

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Section: Introductionmentioning

confidence: 99%

Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

Wang

2018

Journal of Electrical and Computer Engineering

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“…Definition (Wu et al, ) X ={ x n , n =0,1,2,…} is a random process in discrete state space S , where S is a finite state. For all n and arbitrary states I , where n ≥0 and { i 0 , i 1 , i 2 ,…}∈ I , if P ( x n +1 = i n +1 | x 0 = i 0 , x 1 = i 1 ,…, x n = i n )= P ( x n +1 = i n +1 | x n = i n ), where P is a condition probability of x , is satisfied, X is called a finite Markov chain.…”

Section: Slp Convergence Verificationmentioning

confidence: 99%

“…Definition (Wu et al, ) If the condition probability P ( x n +1 = i n +1 | x n = i n ) is independent with time, the chain is called a homogeneous Markov chain.…”

Section: Slp Convergence Verificationmentioning

confidence: 99%

“…Definition (Solis & Wets, ; Wu et al, ) Assume that f is a measurable function, Z ∈ R n , where R n is a measurable set at the n th iteration, and Conditions C1 and C2 are satisfied, where Conditions C1 and C2 are defined as follows: Condition C1: If ζ ∈ Z , then f ( D ( x , ζ )) ≤ f ( x ), where ζ is the iteration result from algorithm D . Condition C2: For B ∈ Z , where v ( B )>0,

\prod_{k = 0}^{\infty} false(1 - u_{k} false(B false) false) = 0

, where v ( B ) is the Lebesgue measure of set B and u k ( B ) is the probability size at the k th iteration of algorithm D in a set B . Let

\lim_{x \to \infty} P false[x^{k} \in R_{e, m} false] = 1

, where x k is a sequence generation of algorithm D from the k th iteration to ∞ interactions. R e , m is an optimal region.…”

Section: Slp Convergence Verificationmentioning

confidence: 99%

“…Currently, many algorithms, such as the PSO algorithm (Pan, Li, Zhou, Li, & Gao, 2013), the ant colony algorithm (Chen & Wu, 2010), the GA (Suzuki, 1995), the chicken swarm optimization algorithm (Wu, Xu, & Kong, 2016), and the bat algorithm (Chen, Peng, He, & Yang, 2018), use…”

Section: Global Convergence For the Slp Algorithmmentioning

confidence: 99%

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Convergence Analysis and Improvement of the Chicken Swarm Optimization Algorithm

Cited by 75 publications

References 29 publications

Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

A novel optimal PID controller autotuning design based on the SLP algorithm

Overview of evolutionary algorithms and neural networks for modern mobile communication

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