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Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems

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NEURAL COMPUTING & APPLICATIONS 2022年第11期34卷 8823-8852页

作者： Abualigah, Laith Ewees, Ahmed A. Al-qaness, Mohammed A. A. Abd Elaziz, Mohamed Yousri, Dalia Ibrahim, Rehab Ali Altalhi, Maryam Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci Pulau 11800 Pinang Malaysia Damietta Univ Dept Comp Dumyat 34517 Egypt Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Fayoum Univ Fac Engn Dept Elect Engn Al Fayyum Egypt Taif Univ Coll Business Adm Dept Management Informat Syst POB 11099 At Taif 21944 Saudi Arabia Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates

Several metaheuristic methods have been applied to tackling various global and engineering optimization problems. However, this method still needs more improvement since they require a suitable balance between exploration and exploitation. Therefore, this study presents an enhancement of the arithmetic optimization algorithm (AOA) as a global optimization method. The developed method, named AOASC, depends on using the sine-cosine algorithm's operators to enhance the exploitation ability of AOA during the searching process. This leads to improving the convergence rate of the developed method toward the optimal solution. Besides, improve the process of avoiding the attraction toward the local point. Besides these behaviors, the quality of the final solution (best one) is improved. To validate the efficiency of the developed method, a set of experiments is conducted, including various optimization problems, such as ten benchmark functions and five engineering optimization problems. Besides, the results of the developed method are compared with other well-known metaheuristic methods. The results showed the high efficiency of the developed method over other methods in terms of performance measures.

关键词： Arithmetic optimization algorithm (AOA) Sine cosine algorithms Levy flight distribution Engineering design problems CEC benchmarks optimization problems

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Ions motion algorithm for solving optimization problems

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APPLIED SOFT COMPUTING 2015年 32卷 72-79页

作者： Javidy, Behzad Hatamlou, Abdolreza Mirjalili, Seyedali Islamic Azad Univ Khoy Branch Dept Comp Sci Khoy Iran Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia

This paper proposes a novel optimization algorithm inspired by the ions motion in nature. In fact, the proposed algorithm mimics the attraction and repulsion of anions and cations to perform optimization. The proposed algorithm is designed in such a way to have the least tuning parameters, low computational complexity, fast convergence, and high local optima avoidance. The performance of this algorithm is benchmarked on 10 standard test functions and compared to four well-known algorithms in the literature. The results demonstrate that the proposed algorithm is able to show very competitive results and has merits in solving challenging optimization problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Ions motion algorithm optimization problems Meta-heuristic algorithms

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Artificial neural networks used in optimization problems

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NEUROCOMPUTING 2018年 272卷 10-16页

作者： Villarrubia, Gabriel De Paz, Juanf. Chamoso, Pablo De la Prieta, Fernando Univ Salamanca BISITE Res Grp Edificio I D I Salamanca 37007 Spain

optimization problems often require the use of optimization methods that permit the minimization or maximization of certain objective functions. Occasionally, the problems that must be optimized are not linear or polynomial;they cannot be precisely resolved, and they must be approximated. In these cases, it is necessary to apply heuristics, which are able to resolve these kinds of problems. Some algorithms linearize the restrictions and objective functions at a specific point of the space by applying derivatives and partial derivatives for some cases, while in other cases evolutionary algorithms are used to approximate the solution. This work proposes the use of artificial neural networks to approximate the objective function in optimization problems to make it possible to apply other techniques to resolve the problem. The objective function is approximated by a non-linear regression that can be used to resolve an optimization problem. The derivate of the new objective function should be polynomial so that the solution of the optimization problem can be calculated. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Neural networks optimization problems Non-linear optimization

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Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 32613-32653页

作者： Abualigah, Laith Oliva, Diego Jia, Heming Gul, Faiza Khodadadi, Nima Hussien, Abdelazim G. Al Shinwan, Mohammad Ezugwu, Absalom E. Abuhaija, Belal Abu Zitar, Raed Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Dept Comp Sci Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia Univ Guadalajara Depto Innovac Basada Informac & El Conocimiento CUCEI Guadalajara Jal Mexico Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Air Univ Dept Elect Engn Aerosp & Avion Campus Kamra Kamra Pakistan Florida Int Univ Dept Civil & Environm Engn Miami FL 33199 USA Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Appl Sci Private Univ Fac Informat Technol Amman 11931 Jordan Northwest Univ Unit Data Sci & Comp 11 Hoffman St ZA-2520 Potchefstroom South Africa Wenzhou Kean Univ Dept Comp Sci Wenzhou 325015 Peoples R China Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi 38044 U Arab Emirates

Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose optimization Algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods;these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods.

关键词： Prairie dog optimization algorithm Dwarf mongoose optimization algorithm Meta-heuristics Benchmark functions optimization problems

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Applications of agent-based models for optimization problems: A literature review

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第5期39卷 6020-6028页

作者： Barbati, M. Bruno, G. Genovese, A. Univ Sheffield Sch Management Sheffield S Yorkshire England Univ Naples Federico II Dept Engn Management Naples Italy

Agent based models (ABM) have been recently applied to solve optimization problems whose domains present several inter-related components in a distributed and heterogeneous environment. In this work we illustrate the state of the art related to the use and to the application of ABM as optimization technique, given their peculiarity in dealing with the representation and the simulation of complex systems. After a description of the approach and a comparison with classical heuristics, an extensive review aimed at evaluating the impact of these methodologies in the Operational Research/Management Science literature is provided. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Agent-based modeling Heuristics optimization problems

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New binary whale optimization algorithm for discrete optimization problems

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ENGINEERING optimization 2020年第6期52卷 945-959页

作者： Hussien, Abdelazim G. Hassanien, Aboul Ella Houssein, Essam H. Amin, Mohamed Azar, Ahmad Taher Fayoum Univ Fac Sci Faiyum Egypt Cairo Univ Fac Comp & Artificial Intelligence Cairo Egypt Sci Res Grp Egypt SRGE Cairo Cairo Egypt Minia Univ Fac Comp & Informat Al Minya Egypt Menoufia Univ Fac Sci Menoufia Egypt Prince Sultan Univ Coll Engn Riyadh Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

The whale optimization algorithm (WOA) is an intelligence-based technique that simulates the hunting behaviour of humpback whales in nature. In this article, an adaptation of the original version of the WOA is made for handling binary optimization problems. For this purpose, two transfer functions (S-shaped and V-shaped) are presented to map a continuous search space to a binary one. To illustrate the functionality and performance of the proposed binary whale optimization algorithm (bWOA), its results when applied on twenty-two benchmark functions, three engineering optimization problems and a real-world travelling salesman problem are found. Furthermore, the proposed bWOA is compared with five well-known metaheuristic algorithms. The experimental results show its superiority in comparison with other state-of-the-art metaheuristics in terms of accuracy and speed. Finally, Wilcoxon's rank-sum non-parametric statistical test is carried out at the 5% significance level to judge whether the results of the proposed algorithm differ from those of the other comparison algorithms in a statistically significant way.

关键词： Whale optimization algorithm binary whale optimization algorithm optimization problems discrete search space metaheuristic algorithms

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Definition of an energy function for the random neural to solve optimization problems

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NEURAL NETWORKS 1998年第4期11卷 731-737页

作者： Jose, A Univ Los Andes Fac Ingn Dept Computac Merida 5101 Venezuela

In this paper, we propose a general energy function for a new neural model, the random neural model of Gelenbe. This model proposes a scheme of interaction between the neurons and not a dynamic equation of the system. We then apply this general energy function on different optimization problems: the graph partitionning problem and the minimum node covering problem. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： optimization problems neural networks energy function graph partitioning minimum node covering

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Boolean constraint satisfaction: complexity results for optimization problems with arbitrary weights

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THEORETICAL COMPUTER SCIENCE 2000年第1-2期244卷 189-203页

作者： Jonsson, P Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden

A boolean constraint satisfaction problem consists of some finite set of constraints (i.e., functions from 0/1-vectors to {0, 1}) and an instance of such a problem is a set of constraints applied to specified subsets of n boolean variables. The goal is to find an assignment to the variables which satisfy all constraint applications. The computational complexity of optimization problems in connection with such problems has been studied extensively but the results have relied on the assumption that the weights are non-negative. The goal of this article is to study variants of these optimization problems where arbitrary weights are allowed. For the four problems that we consider, we give necessary and sufficient conditions for when the problems can be solved in polynomial time. In addition, we show that the problems are NP-equivalent in all other cases. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： computational complexity Boolean constraints optimization problems

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Application of improved hybrid whale optimization algorithm to optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年第17期35卷 12433-12451页

作者： Uzer, Mustafa Serter Inan, Onur Selcuk Univ Ilgin Vocat Sch Elect & Automat Konya Turkiye Selcuk Univ Fac Technol Comp Engn Konya Turkiye

The Whale optimization Algorithm (WOA) is one of the recent meta-heuristic algorithms. WOA has advantages such as an exploration mechanism that leads towards the global optimum, a suitable balance between exploration and exploitation that avoids the local optimum, and a very good exploitation capability. In this study, five new hybrid algorithms are proposed to develop these advantages. Two of them are developed by combining WOA and Particle Swarm optimization (PSO) algorithms, and three of them are developed by adding the Levy flight algorithm to this combination in different ways. The proposed algorithms have been tested with 23 mathematical optimization problems, and in order to make a more accurate comparison, the average optimization results and corresponding standard deviation results are calculated by running these algorithms 30 times for each optimization problem. The proposed algorithms' performances were evaluated among themselves, and the WOALFVWPSO algorithm performed better among these algorithms. This proposed algorithm has been first compared with WOA and PSO, then with other algorithms in the literature. According to WOA and PSO, the proposed algorithm performs better in 19 of 23 mathematical optimization problems, and according to other literature, it performs better in 15 of 23 problems. Also, the proposed algorithm has been applied to the pressure vessel design engineering problem and achieved the best result compared to other algorithms in the literature. It has been proven that the WOALFVWPSO algorithm provides competitive solutions for most optimization problems when compared to meta-heuristic algorithms in the literature.

关键词： Whale optimization algorithm Particle swarm optimization Levy flight optimization problems Benchmark functions

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An efficient modified artificial electric field algorithm for solving optimization problems and parameter estimation of fuel cell

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第14期45卷 20199-20218页

作者： Houssein, Essam H. Hashim, Fatma A. Ferahtia, Seydali Rezk, Hegazy Minia Univ Fac Comp & Informat Al Minya Egypt Helwan Univ Fac Engn Cairo Egypt Univ Msila Dept Elect Engn Lab Anal Signaux & Syst Msila Algeria Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Minia Univ Fac Engn Al Minya Egypt

The artificial electric field algorithm (AEFA) is a recent physics population-based optimization approach inspired by Coulomb's law of electrostatic force and Newton's law of motion. In this paper, an alternative version of AEFA called mAEFA is proposed to boost the searchability and the balance between the explorations to the exploitation of the original AEFA. To escape dropping on the local points in the mAEFA, three efficient strategies for instance;modified local escaping operator (MLEO), levy flight (LF), and opposition-based learning (OBL), are in conjunction with the original AEFA. The convergence rate will be improved when the best agent is identified;thus, stagnation at a local solution can be efficiently avoided. To assess the performance of the proposed mAEFA, it has been evaluated over the CEC'2020 test functions. Furthermore, a robust methodology based on mAEEA is proposed to identify the best parameters of PEM fuel cell (PEMFC). The model of the PEMFC includes nonlinear characteristics that involve several unknown design variables. Thus, it is challenging to develop an accurate model. There are seven design variables to be tuned to reach the targeted dependable model. Two different types of PEMFCs: NedStack PS6 and SR-12 500 W were used to demonstrate the superiority of the mAEEA. Throughout the optimization process, the unidentified parameters of PEMFC are appointed to be decision variables. But the objective function, which necessary to be least is represented by the SSE between the calculated PEMFC voltage and the experimental one. Nine recent optimizers are used in the comparison with the proposed mAEEA. According to the main findings, the advantage of the proposed mAEEA in determining the best PEMFC parameters is verified compared to the other optimizers. Lowest SSE, lowest RMSE, minimum stranded deviation, maximum efficiency, and high coefficient of determination are achieved by the proposed mAEEA.

关键词： artificial electric field algorithm CEC'2020 test functions fuel cell optimization problems parameter estimation

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