A New Teaching–Learning-based Chicken Swarm Optimization Algorithm

Abstract

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.

A hybrid ant lion optimization chicken swarm optimization algorithm for charger placement problem

“…The performance of ALO CSO algorithm in dealing with the economic load dispatch problem is compared with that of the other algorithms like RCCRO, CSO TLBO, and DE. The results of RCCRO, DE, CSO TLBO, and TLBO are taken from [35]. The mean fitness values over 50 independent trials obtained by these algorithms are presented in Table 5, from which the superiority of ALO CSO over TLBO, RRCRO, CSO TLBO, and DE in this case study is clearly demonstrated.…”

“…The economic load dispatch problem is attacked for 38 generator test system [44] by ALO CSO. The general parameter settings are the same as in [35]. The performance of ALO CSO algorithm in dealing with the economic load dispatch problem is compared with that of the other algorithms like RCCRO, CSO TLBO, and DE.…”

“…Mohanty et al (2021) used Jaya algorithm with the cost as the objective function [19]. Reddy Table 1 Variants of CSO algorithm Introduction of improved search strategy with Levy flight in the hen's location update in CSO [32] 2020 Hybridization of CSO with Tabu search [33] 2020 Modified update of chicks by adding inertia weights in CSO [34] 2020 Development of quantum inspired CSO [35] 2020 Modified update of rooster and introduction of novel constraint handling mechanism in CSO 2020) presented a new PSO for the optimal placement of charging stations in unbalanced radial distribution network with the power loss as the objective function [20]. Amini et al (2017) proposed an optimal placement strategy for chargers in parking lots using GA [21].…”

An Efficient Chicken Search Optimization Algorithm for the Optimal Design of Fuzzy Controllers

“…In recent decades the Chicken Search Optimization (CSO) algorithm has shown good results and the efficiency of this bio-inspired algorithm has been verified in different problems. For example, in [1], Al Shayokh et al presented a bio inspired distributed Wireless Sensor Network (WSN) localization based on chicken swarm optimization; in [2], Banerjee et al presented an improved serially concatenated convolution turbo code (SCCTC) using chicken swarm optimization; in [3], Chen et al presented a penalty function with a modified chicken swarm optimization for constrained optimization; in [4], Deb et al presented a Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem; in [5], Deb et al showed a new Teaching-Learning-based Chicken Swarm Optimization Algorithm; in [6], Deb et al presented some recent studies on Chicken Swarm Optimization Algorithms; and in [7], Hafez et al presented an innovative approach for feature selection based on Chicken Swarm Optimization. In addition, in [8], Kurozawa et al showed an optimization of the enzymatic hydrolysis of chicken meat using response surface methodology; in [9], Lin et al presented a robust recurrent wavelet neural network controller with improved particle swarm optimization for linear synchronous motor drive; in [10], Qu et al presented a Chicken Swarm Optimization based on elite opposition-based learning; in [11], Wang et al showed an improved Chicken Swarm Algorithm based on chaos theory and its application in wind power interval prediction; and in [12], Wu et al presented an improved Chicken Swarm Optimization.…”

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

“…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. Similarly, TLBO is successfully used to cope with parameter optimization of machining process [28], transmission expansion planning [29], economic load dispatch problem [30], optimization of heat exchangers [31], optimal configuration of microgrid [32], optimization of space trusses [33], groundwater prediction [34], energy demand estimation [35], parameter extraction of photovoltaic models [36], glazing system design [37], PID controller design [38], wind speed forecasting [39], energy performance assessment of buildings [40], etc.…”