An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Abstract

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.

A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization

“…(2) Some classic mathematical and physical theories have been used to enhance the performance of NIOAs, such as fuzzy theory [ 133 ], chaos theory [ 56 ] and quantum theory [ 48 , 132 ], and the exploration and exploitation abilities of NIOAs.…”

A Poisson Hidden Markov Model and Fuzzy based Chicken Swarm Optimization algorithm for efficient fault node detection in wireless sensor network

“…The bio-inspired chicken swarm optimization (CSO) algorithm [23,24] is an advanced intelligent algorithm which represents the different behaviors related to chickens, hens and cocks in their process of food search. It is a random search algorithm that resembles the hierarchical structure and behaviour of the chicken swarm.…”

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

“…Next, the fuzzification technique is performed with the major sets of fuzzy value and membership functions of both systems inputs and outputs. Mamdani inference mechanism is used to identify relationships between input and output 26 , 27 .…”

“…A mix of conventional and flexible procedures is developed and provided to effectively construct and get the robotic handler that tackles the difficulties of similarity between regular and Fuzzy logic methods. Depending on the input variables, the fuzzy logical control system has been designed with two inputs to identify the selection of input variables, which is a more single task 9 . The new fuzzy input PD and PI control error change and error control signals, with similar input variables by type PI, position, and speed controller type 10 – 13 .…”