Cso: An Improved Snake Optimizer with Chaotic Maps DOI

Junlei Wang,

Mengxue Dong,

Maosen Xu

et al.

Published: Jan. 1, 2023

A new meta-heuristic algorithm that has demonstrated strong performance on optimization problems is called the Snake Optimizer (SO). Nevertheless, compared to other methods, SO a number of drawbacks, such as slow convergence, narrow search solution space, and easy settle into local optimal solutions. To address these issues, this work proposes an improved snake optimizer (CSO) introduces chaotic (CLS) procedure. The goal implementing take advantage chaos's traversal non-repetitive properties broaden population's diversity enhance algorithmic performance. In study, we embedded ten mappings process tested effectiveness CSO 23 benchmark functions with different characteristics CEC2022 function set. Furthermore, evaluate CSO's against six competitive methods traditional algorithm. outcomes demonstrate issue, Improved appropriate mapping performs better than regular its rivals.

Language: Английский

Using Sparrow Search Hunting Mechanism to Improve Water Wave Algorithm DOI
Haotian Li, Baohang Zhang, Jiayi Li

et al.

Published: Dec. 17, 2021

The water wave optimization (WWO) algorithm is a new cluster intelligence search method. It has the advantages of small population size and simple parameter configuration. used to build an efficient mechanism for searching in high-dimensional solution spaces. However, it proclivity becoming stuck local optima. Coincidentally, sparrow (SSA) good exploration ability. By combining WWO SSA, we propose hybrid algorithm, called WWOSSA. experimental results WWOSSA based on 29 benchmark functions IEEE CEC2017 have ability fast convergence rate.

Language: Английский

Citations

12
Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for Engineering Problems DOI Creative Commons
Yupeng Li, Dong Zhao, Ali Asghar Heidari

et al.

Journal of Bionic Engineering, Journal Year: 2024, Volume and Issue: 21(2), P. 1055 - 1091

Published: March 1, 2024

Abstract In recent years, with the increasing demand for social production, engineering design problems have gradually become more and complex. Many novel well-performing meta-heuristic algorithms been studied developed to cope this problem. Among them, Spherical Evolutionary Algorithm (SE) is one of classical representative methods that proposed in years admirable optimization performance. However, it tends stagnate prematurely local optima solving some specific problems. Therefore, paper proposes an SE variant integrating Cross-search Mutation (CSM) Gaussian Backbone Strategy (GBS), called CGSE. study, CSM can enhance its learning ability, which strengthens utilization rate on effective information; GBS cooperates original rules further improve convergence effect SE. To objectively demonstrate core advantages CGSE, designs a series global experiments based IEEE CEC2017, CGSE used solve six constraints. The final experimental results fully showcase that, compared existing well-known methods, has very significant competitive advantage tasks certain practical value real applications. promising first-rate algorithm good potential strength field design.

Language: Английский

Citations

1
PMSOMA: optical microscope algorithm based on piecewise linear chaotic mapping and sparse adaptive exploration DOI Creative Commons

Linyi Guo,

Wei Gu

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 6, 2024

Language: Английский

Citations

1
An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool DOI Open Access
Lin Yang, Zhe Xu,

Yanting Liu

et al.

Symmetry, Journal Year: 2022, Volume and Issue: 14(6), P. 1227 - 1227

Published: June 13, 2022

Big Data is impacting and changing the way we live, its core lies in use of machine learning to extract valuable information from huge amounts data. Optimization problems are a common problem many steps learning. In face complex optimization problems, evolutionary computation has shown advantages over traditional methods. Therefore, researchers working on improving performance algorithms for solving various The equilibrium optimizer (EO) member inspired by mass balance model environmental engineering. Using particles their concentrations as search agents, it simulates process finding states optimization. this paper, propose an improved (IEO) based decreasing pool. IEO provides more sources particle updates maintains higher population diversity. It can discard some exploration later stages enhance exploitation, thus achieving better balance. verified using 29 benchmark functions IEEE CEC2017, dynamic economic dispatch problem, spacecraft trajectory artificial neural network training problem. addition, changes diversity computational complexity brought proposed method analyzed.

Language: Английский

Citations

7
Umbrellalike Hierarchical Artificial Bee Colony Algorithm DOI Open Access
Tao Zheng,

Han ZHANG,

Baohang Zhang

et al.

IEICE Transactions on Information and Systems, Journal Year: 2023, Volume and Issue: E106.D(3), P. 410 - 418

Published: Feb. 28, 2023

Many optimisation algorithms improve the algorithm from perspective of population structure. However, most improvement methods simply add hierarchical structure to original structure, which fails fundamentally change its In this paper, we propose an umbrellalike artificial bee colony (UHABC). For first time, a historical information layer is added (ABC), and allowed interact with other layers generate information. To verify effectiveness proposed algorithm, compare it five representative meta-heuristic on IEEE CEC2017. The experimental results statistical analysis show that mechanism effectively improves performance ABC.

Language: Английский

Citations

3
Swarm Exploration Mechanism-Based Distributed Water Wave Optimization DOI Creative Commons
Haotian Li, Haichuan Yang, Baohang Zhang

et al.

International Journal of Computational Intelligence Systems, Journal Year: 2023, Volume and Issue: 16(1)

Published: May 9, 2023

Abstract Using sparrow search hunting mechanism to improve water wave algorithm (WWOSSA), which combines the optimization (WWO) and (SSA), has good ability fast convergence speed. However, it still suffers from insufficient exploration is easy fall into local optimum. In this study, we propose a new for distributed population structure, called swarm mechanism-based (DWSA). DWSA, an information exchange component optimal individual evolution are designed between individuals. This multi-part interaction structure can help establish balance exploitation more effectively. We contrast DWSA with original algorithms WWOSSA other meta-heuristics in order show effectiveness of DWSA. The test set consists 22 actual issues CEC2011 29 benchmark functions CEC2017 functions. addition, experimental comparison parameter values introduced included. According results, proposed performs substantially better than its competitors. Assessments diversity landscape trajectory also confirmed DWSA’s outstanding convergence.

Language: Английский

Citations

2
Enerji Sistemlerinde Metasezgisel Optimizasyon Teknikleri: Yenilikçi Algoritmalar ve Uygulama Alanları DOI Creative Commons
Mert Ökten

Sürdürülebilir Mühendislik Uygulamaları ve Teknolojik Gelişmeler Dergisi, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 25, 2024

Optimizasyon, tüm olası alternatifler arasından bir problemin en optimal çözümünü belirleme sürecidir. Enerji sistemlerinde metasezgisel optimizasyon algoritmaları, karmaşık enerji problemlerini çözmede önemli rol oynamaktadır. Metasezgisel genetik algoritmalar, parçacık sürü optimizasyonu, simüle edilen tavlama, karınca kolonisi optimizasyonu gibi doğal süreçlerden esinlenerek geliştirilen ve genellikle bilgisayar tabanlı modellerle kullanılan özel yöntemleridir. büyük veri setleriyle çalışabilir farklı kısıtlamalar altında optimize edilmesi gereken çok sayıda değişkeni ele alabilirler. Bu nedenle sektöründe sürdürülebilirlik, verimlilik karlılık açısından öneme sahiptirler. verimliliğini artırmak, maliyetini azaltmak, üretimi, dağıtımı, tüketimi depolanması sistemlerinin bileşenlerini etmek için, yenilenebilir kaynaklarını entegre karbon ayak izini azaltmak çeşitli hedeflere ulaşmak için kullanılmaktadırlar. çalışmada, sistemleri uygulamalarında algoritmalarının kullanımı örnekler üzerinden incelenmiştir. algoritmaların ile problemlerin çözümlerinin daha kolaya indirgendiği görülmüştür.

Citations

0
An Adaptive Dimension Weighting Spherical Evolution to Solve Continuous Optimization Problems DOI Creative Commons
Yifei Yang, Sichen Tao, Shibo Dong

et al.

Mathematics, Journal Year: 2023, Volume and Issue: 11(17), P. 3733 - 3733

Published: Aug. 30, 2023

The spherical evolution algorithm (SE) is a unique proposed in recent years and widely applied to new energy optimization problems with notable achievements. However, the existing improvements based on SE are deemed insufficient due challenges arising from multiple choices of operators utilization search method. In this paper, we introduce an enhancement method that incorporates weights individuals’ dimensions affected by individual fitness during iteration process, aiming improve adaptively balancing tradeoff between exploitation exploration convergence. This achieved reducing randomness dimension selection enhancing retention historical information iterative process algorithm. improvement named DWSE. To evaluate effectiveness DWSE, study, apply it CEC2017 standard test set, CEC2013 large-scale global 22 real-world CEC2011. experimental results substantiate DWSE achieving improvement.

Language: Английский

Citations

1
A Lottery-based Spherical Evolution Algorithm with Elite Retention Strategy DOI
Jiayi Li, Zihang Zhang, Zhenyu Lei

et al.

Published: Aug. 1, 2022

Spherical evolution (SE) is a recently proposed meta-heuristic algorithm. Its special search approach has been proved to be very effective in exploring the space. SE powerful for optimization, but still room improvement due some promising solutions usually fail survive into next generation. To alleviate this issue, we innovatively design novel lottery-based elite retention strategy and propose lottery spherical algorithm (LESE). verify effectiveness of LESE, experimentally compare it with original other representative meta-heuristics algorithms. We use 30 benchmark functions from IEEE CEC2017 as test set our experiments. The LESE demonstrated by analyzing experimental results perspectives solution accuracy, convergence speed, distribution, dynamics.

Language: Английский

Citations

2
An Immigration Strategy-based Spherical Search Algorithm DOI

Qingya Sui,

Sichen Tao,

Lin Zhong

et al.

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), Journal Year: 2022, Volume and Issue: unknown

Published: Dec. 15, 2022

The spherical search algorithm (SS) is a novel and competitive applied to real-world problems. However, the population of SS divided equally, which requires large number computation resources for different To alleviate issues, we propose an immigration strategy-based algorithm, namely ISS. ISS adaptively selects individuals that are successfully updated in each generation replaces operator next iteration. experiments were conducted on 30 benchmark functions from IEEE CEC2017. compared with verify effectiveness proposed adaptive strategy. Additionally, classical differential evolutionary (DE) state-of-the-art triple archive particle swarm optimization (TAPSO) test its performance further. diversity analyzed discuss effect experimental results demonstrate strategy quite effective, significantly better than peer's algorithms.

Language: Английский

Citations

1