Heap-Based Optimizer Algorithm with Chaotic Search for Nonlinear Programming Problem Global Solution

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-14 DOI:10.1007/s44196-023-00327-1

Rizk M. Rizk-Allah, Islam M. Eldesoky, Ekram A. Aboali, Sarah M. Nasr

{"title":"Heap-Based Optimizer Algorithm with Chaotic Search for Nonlinear Programming Problem Global Solution","authors":"Rizk M. Rizk-Allah, Islam M. Eldesoky, Ekram A. Aboali, Sarah M. Nasr","doi":"10.1007/s44196-023-00327-1","DOIUrl":null,"url":null,"abstract":"Abstract In this paper, a heap-based optimizer algorithm with chaotic search has been presented for the global solution of nonlinear programming problems. Heap-based optimizer (HBO) is a modern human social behavior-influenced algorithm that has been presented as an effective method to solve nonlinear programming problems. One of the difficulties that faces HBO is that it falls into locally optimal solutions and does not reach the global solution. To recompense the disadvantages of such modern algorithm, we integrate a heap-based optimizer with a chaotic search to reach the global optimization for nonlinear programming problems. The proposed algorithm displays the advantages of both modern techniques. The robustness of the proposed algorithm is inspected on a wide scale of different 42 problems including unimodal, multi-modal test problems, and CEC-C06 2019 benchmark problems. The comprehensive results have shown that the proposed algorithm effectively deals with nonlinear programming problems compared with 11 highly cited algorithms in addressing the tasks of optimization. As well as the rapid performance of the proposed algorithm in treating nonlinear programming problems has been proved as the proposed algorithm has taken less time to find the global solution.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"111 1","pages":"0"},"PeriodicalIF":2.9000,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computational Intelligence Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s44196-023-00327-1","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Abstract In this paper, a heap-based optimizer algorithm with chaotic search has been presented for the global solution of nonlinear programming problems. Heap-based optimizer (HBO) is a modern human social behavior-influenced algorithm that has been presented as an effective method to solve nonlinear programming problems. One of the difficulties that faces HBO is that it falls into locally optimal solutions and does not reach the global solution. To recompense the disadvantages of such modern algorithm, we integrate a heap-based optimizer with a chaotic search to reach the global optimization for nonlinear programming problems. The proposed algorithm displays the advantages of both modern techniques. The robustness of the proposed algorithm is inspected on a wide scale of different 42 problems including unimodal, multi-modal test problems, and CEC-C06 2019 benchmark problems. The comprehensive results have shown that the proposed algorithm effectively deals with nonlinear programming problems compared with 11 highly cited algorithms in addressing the tasks of optimization. As well as the rapid performance of the proposed algorithm in treating nonlinear programming problems has been proved as the proposed algorithm has taken less time to find the global solution.

查看原文本刊更多论文

基于混沌搜索的堆优化算法求解非线性规划问题

摘要针对非线性规划问题的全局解，提出了一种基于混沌搜索的堆优化算法。基于堆的优化器(HBO)是一种影响人类社会行为的现代算法，是解决非线性规划问题的有效方法。HBO面临的困难之一是陷入局部最优解，无法达到全局解。为了弥补这种现代算法的不足，我们将基于堆的优化器与混沌搜索相结合，以达到非线性规划问题的全局优化。该算法综合了两种现代技术的优点。在包括单峰、多峰测试问题和CEC-C06 2019基准问题在内的42个不同问题上，对所提出算法的鲁棒性进行了广泛的检验。综合结果表明，与11种高引用算法相比，该算法在解决优化任务方面能有效地处理非线性规划问题。此外，该算法在求解非线性规划问题时所花费的时间较短，证明了其快速的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Computational Intelligence Systems 工程技术-计算机：跨学科应用

自引率

3.40%

发文量

期刊介绍： The International Journal of Computational Intelligence Systems publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics: -Autonomous reasoning- Bio-informatics- Cloud computing- Condition monitoring- Data science- Data mining- Data visualization- Decision support systems- Fault diagnosis- Intelligent information retrieval- Human-machine interaction and interfaces- Image processing- Internet and networks- Noise analysis- Pattern recognition- Prediction systems- Power (nuclear) safety systems- Process and system control- Real-time systems- Risk analysis and safety-related issues- Robotics- Signal and image processing- IoT and smart environments- Systems integration- System control- System modelling and optimization- Telecommunications- Time series prediction- Warning systems- Virtual reality- Web intelligence- Deep learning