An opposition-based particle swarm optimization algorithm for noisy environments

Zhou, MengChu; Zhao, Zeyu; Xiong, Caifei; Kang, Qi

doi:10.1109/icnsc.2018.8361279

Cited by 13 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The improved chicken swarm optimization algorithm in [25] is abbreviated as ICSO-2. The algorithm in [42] is abbreviated as OBL-PSOGD. The algorithm in [43] is abbreviated as ICS.…”

Section: H Analysis Of Test Resultsmentioning

confidence: 99%

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

“…The improved chicken swarm optimization algorithm in [25] is abbreviated as ICSO-2. The algorithm in [42] is abbreviated as OBL-PSOGD. The algorithm in [43] is abbreviated as ICS.…”

Section: H Analysis Of Test Resultsmentioning

confidence: 99%

Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

et al. 2019

View full text Add to dashboard Cite

show abstract

“…To verify the performance of AVC-IMOA, four MOA algorithms and two meta-heuristic algorithms are selected for comparison. These are the algorithms involved in the comparison: standard MOA (MOA) [13], MOA with velocity processing and gravity coefficient (VG-MOA) [13], MOA with improved velocity update formula (IMOA) [18], MOA with oppositionbased learning rules (OBL_MO) [19], sparrow search algorithm (SSA) and opposition-based learning particle swarm optimization by group decision-making (OBLPSOGD) [27]. The parameters of the algorithms involved in the comparison are all the values found in the original literature, and the specific parameter values are shown in Table 2.…”

Section: Parameter Settingmentioning

confidence: 99%

Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network

et al. 2022

View full text Add to dashboard Cite

Focusing on the issues of slow convergence speed and the ease of falling into a local optimum when optimizing the weights and thresholds of a back-propagation artificial neural network (BPANN) by the gradient method, a prediction method for pork supply based on an improved mayfly optimization algorithm (MOA) and BPANN is proposed. Firstly, in order to improve the performance of MOA, an improved mayfly optimization algorithm with an adaptive visibility coefficient (AVC-IMOA) is introduced. Secondly, AVC-IMOA is used to optimize the weights and thresholds of a BPANN (AVC-IMOA_BP). Thirdly, the trained BPANN and the statistical data are adopted to predict the pork supply in Heilongjiang Province from 2000 to 2020. Finally, to demonstrate the effectiveness of the proposed method for predicting pork supply, the pork supply in Heilongjiang Province was predicted by using AVC-IMOA_BP, a BPANN based on the gradient descent method and a BPANN based on a mixed-strategy whale optimization algorithm (MSWOA_BP), a BPANN based on an artificial bee colony algorithm (ABC_BP) and a BPANN based on a firefly algorithm and sparrow search algorithm (FASSA_BP) in the literature. The results show that the prediction accuracy of the proposed method based on AVC-IMOA and a BPANN is obviously better than those of MSWOA_BP, ABC_BP and FASSA_BP, thus verifying the superior performance of AVC-IMOA_BP.

show abstract

“…In the past decades, OBL scheme was widely modified and implemented into MSAs to enhance the performance of the optimization algorithm in dealing with optimization problems with different complexity level. In [31], a probabilistic opposition-based learning (OBL) scheme was designed into PSO to tackle noisy optimization problems effectively. A number of best particles were picked from the current swarm and the opposite swarm generated by the adopted OBL scheme, in order to preserve the swarm diversity in noisy environments.…”

Section: Metaheuristic Search Algorithms With Opposition-based Learning Schemementioning

confidence: 99%

Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems

et al. 2021

View full text Add to dashboard Cite

Different variants of particle swarm optimization (PSO) algorithms were introduced in recent years with various improvements to tackle different types of optimization problems more robustly. However, the conventional initialization scheme tends to generate an initial population with relatively inferior solution due to the random guess mechanism. In this paper, a PSO variant known as modified PSO with chaotic initialization scheme is introduced to solve unconstrained global optimization problems more effectively, by generating a more promising initial population. Experimental studies are conducted to assess and compare the optimization performance of the proposed algorithm with four existing well-establised PSO variants using seven test functions. The proposed algorithm is observed to outperform its competitors in solving the selected test problems.

show abstract

An opposition-based particle swarm optimization algorithm for noisy environments

Cited by 13 publications

References 28 publications

Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network

Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems

Contact Info

Product

Resources

About