Adaptive Simplified Chicken Swarm Optimization Based on Inverted S‐Shaped Inertia Weight

Front. Bioeng. Biotechnol.

Ding

Yang

et al. 2022

Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.

Section: Essentials Of the Oplssamentioning

confidence: 99%

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

Front. Bioeng. Biotechnol.

Ding

Yang

et al. 2022

“…It is worth noting that the reason why N re and N ed are set to 10 in BFA is to ensure that the maximum number of iterations is 10000. The parameter settings of PSO and GA are derived from the comparison algorithms mentioned in the literature [ 28 , 32 ], respectively.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…To compare the performance of various algorithms more reasonably, this section uses the Friedman test to test the performance of the abovementioned 7 algorithms (ICSO, ICSOII, PSO, CSO, GA, BFA, and AFSA) from a statistical point of view. The Friedman test is a nonparametric test method, which is often used to test the performance of algorithms due to its simple operation and lax requirements on the test data [ 32 , 33 , 36 ]. For the minimum optimization problem, the smaller the average ranking of the algorithm, the better its performance.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“… Modification of position update formulas of the chicken swarm. For example, Gu et al improved the CSO algorithm by introducing an adaptive update factor and designing the inertia weight and successfully applied it to parameter estimation of the Richards model [ 32 ]. Considering that the imbalance between the diversity and intensification of the population may affect the performance of the CSO algorithm, Lin et al proposed an improved CSO algorithm by modifying the position update formulas of the roosters and hens.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Chicken Swarm Optimization Algorithm for Solving Multimodal Optimization Problems

Liang

Computational Intelligence and Neuroscience

2022

To solve the premature convergence problem of the standard chicken swarm optimization (CSO) algorithm in dealing with multimodal optimization problems, an improved chicken swarm optimization (ICSO) algorithm is proposed by referring to the ideas of bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) algorithm. First, in order to improve the depth search ability of the algorithm, considering that the chicks have the weakest optimization ability in the whole chicken swarm, the replication operation of BFA is introduced. In the role update process of the chicken swarm, the chicks are replaced by the same number of chickens with the strongest optimization ability. Then, to maintain the population diversity, the elimination-dispersal operation is introduced to disperse the chicks that have performed the replication operation to any position in the search space according to a certain probability. Finally, the PSO algorithm is integrated to improve the global optimization ability of the algorithm. The experimental results on the CEC2014 benchmark function test suite show that the proposed algorithm has good performance in most test functions, and its optimization accuracy and convergence performance are also better than BFA, artificial fish swarm algorithm (AFSA), genetic algorithm (GA), and PSO algorithm, etc. In addition, the ICSO is also utilized to solve the welded beam design problem, and the experimental results indicate that the proposed algorithm has obvious advantages over other comparison algorithms. Its disadvantage is that it is not suitable for dealing with large-scale optimization problems.

“…Although the convergence speed and stability were improved, the solution accuracy is still unsatisfactory. Gu et al realized the solution to high-dimensional complex function optimization problems by removing the chicks in the chicken swarm and introducing an inverted S-shaped inertial weight to construct an adaptive simplified CSO algorithm [ 28 ]. Although the proposed algorithm is significantly better than some other algorithms in solution accuracy, there is still room for improvement in convergence speed.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization

Liang

2023

Biomimetics

With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.