Improved Spherical Evolution Algorithm with Intelligent Tuning of Search Space

Published: April 7, 2023

The spherical evolution (SE) search algorithm has a more novel style than the common meta-heuristic algorithm. In contrast to traditional hypercube model, SE uses approach and achieves very good results. Although is effective, it suffers from problem of sometimes slow convergence due large space imbalance between development exploration capability. To address this, we innovatively proposed strategy intelligently adjust based on population structure information improved (ISE). Review effectiveness our strategy, compare ISE with several other well-known algorithms. Our problems 30 different solution spaces IEEE CEC2017 benchmark serve as test set for experiments. experimental results show that significant advantage over

Language: Английский

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L-CJADE: Differential Evolution Algorithm Based On A Linearly Decreasing Population Structure

2022 7th International Conference on Computer and Communication Systems (ICCCS), Journal Year: 2023, Volume and Issue: unknown, P. 875 - 879

Published: April 21, 2023

Chaotic local search based differential evolutionary optimization algorithm (CJADE) is an improved on JADE. It incorporates the ideas of chaotic mapping and to enhance global capabilities algorithms. Although performance this has been greatly compared with original algorithm, there are still some shortcomings, such as insufficient exploitation ability, slow convergence, exploration easy lead optimum. To solve these problems, we propose a linearly decreasing strategy named L-CJADE, which changes population structure introduces balance relationship between development ability making more stable suitable for practical problems. The L-CJADE was other state-of-the-art algorithms in terms problem sets. results show that it better faster convergence speed along relatively high stability

Language: Английский

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Multi-Chaotic Oppositional Differential Evolution Hybridized with Arithmetic Optimization Algorithm for Global Optimization Problems

Published: Jan. 1, 2023

Differential evolution (DE) is a widely used optimization algorithm known for its simplicity and fast convergence. However, effectiveness in solving diverse problems relies on the quality of initial population ability to balance exploration exploitation searches. We present new variant called Multi-Chaotic Oppositional Evolution Hybridized with Arithmetic Optimization Algorithm (MCO-DEHAOA) these challenges. MCO-DEHAOA incorporates two crucial enhancements: modified initialization technique MCO mutation scheme. leverages multiple chaotic maps oppositional-based learning generate an improved solution quality. Additionally, scheme combines DE/rand/1 Addition Subtraction operators from AOA, leading better balancing exploitation. This hybridization allows dynamically adjust strategy, emphasizing early stages gradually shifting towards refine solutions as progresses. To evaluate performance MCO-DEHAOA, we conducted extensive simulations using CEC 2017 benchmark functions three real-world engineering problems. The results demonstrate that surpasses state-of-the-art algorithms terms accuracy, reliability, efficiency. These findings highlight efficacy powerful tool wide range applications.

Language: Английский

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Cso: An Improved Snake Optimizer with Chaotic Maps

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

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