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Accelerating sailfish optimization applied to unconstrained optimization problems on graphical processing unit

ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2022年 32卷

作者： Naji, Hamid Reza Shadravan, Soodeh Jafarabadi, Hossien Mousa Momeni, Hossien Grad Univ Adv Technol Dept Elect & Comp Engn Kerman Iran Islamic Azad Univ Dept Comp Engn Bardsir Branch Bardsir Iran

The Sailfish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternates their attacks on group of prey. The SFO algorithm takes advantage of using a simple method for providing the dynamic balance between exploration and exploitation phases, creating the swarm diversity, avoiding local optima, and guaranteeing high convergence speed. However, taking a lot of time to solve optimization problems has become a challenge for metaheuristic algorithms. Due to independence of the metaheuristics components, parallel processing is a good option to reduce the computational time and to find high quality solutions that are close to the optimum with an acceptable cost. Nowadays, combination of parallel processing and metaheuristic algorithms can provide high performance solutions to quickly solve combinatorial optimization problems. In this paper, we elaborate a novel GPU based and accelerated method of sailfish optimizer (ASFO), which improves the execution time and speedup while maintaining the results of optimization in high quality. In depth of study, we present the implementation details and performance observations of ASFO algorithm. Also, a comparative study of accelerated and sequential SFO is performed on a set of standard benchmark optimization functions and it compared with other parallel algorithms to show the speed of proposed algorithm for solving unconstrained optimization problems. The results indicate the ability of proposed approach in continuous, non-separable, non-convex and scalable optimization problems. (c) 2021 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Sailfish Optimizer (SFO) Accelerated Sailfish Optimizer (ASFO) unconstrained optimization problems Parallel processing Shared memory Graphic processing units CUDA

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Tactical flight optimizer: a novel optimization technique tested on mathematical, mechanical, and structural optimization problems

MATERIALS TESTING

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MATERIALS TESTING 2025年第2期67卷 330-352页

作者： Mortazavi, Ali Moloodpoor, Mahsa Ege Univ Mech Engn Dept Izmir Turkiye Izmir Democracy Univ Civil Engn Dept Izmir Turkiye

The current study presents a novel gradient-free metaheuristic search algorithm named Tactical Flight Optimizer (TFO), tailored to meet the growing need for high-performance optimization techniques in solving complex engineering and mathematical problems. The main contribution of this study is the development of a method that simulates tactical air combat formations, offering a sophisticated alternative to conventional search algorithms. In the proposed method, the location of each agent is updated based on a resultant vector derived from three updating vectors. The updating vectors incorporate total information stored by the agents in each iteration. Consequently, the navigation process is guided by a more logical mechanism rather than a simple random process. The search performance of the TFO is initially benchmarked by solving a set of constrained mathematical functions. Subsequently, it is evaluated by addressing a suite of constrained mechanical and structural problems, containing both discrete and continuous decision variables. The obtained results are compared with five other well-stablished metaheuristic techniques. Acquired numerical results indicate that the TFO algorithm can provide promising results in solving both mathematical and engineering problems in terms of computational cost, accuracy, and stability.

关键词： optimization techniques metaheuristic algorithms constrained optimization problems unconstrained optimization problems discrete and continuous variables

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A nonmonotone trust region method based on nonincreasing technique of, weighted average of the successive function values

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2007年第1期209卷 97-108页

作者： Mo, Jiangtao Liu, Chunyan Yan, Shicui Guangxi Univ Coll Math & Informat Sci Nanning 530004 Peoples R China

In this paper we propose a nonmonotone trust region method. Unlike traditional nonmonotone trust region method, the nonmonotone technique applied to our method is based on the nonmonotone line search technique proposed by Zhang and Hager [A nonmonotone line search technique and its application to unconstrained optimization, SIAM J. Optim. 14(4) (2004) 1043-1056] instead of that presented by Grippo et al. [A nonmonotone line search technique for Newton's method, SIAM J. Numer. Anal. 23(4) (1986) 707-7161. So the method requires nonincreasing of a special weighted average of the successive function values. Global and superlinear convergence of the method are proved under suitable conditions. Preliminary numerical results show that the method is efficient for unconstrained optimization problems. (c) 2006 Elsevier B.V. All rights reserved.

关键词： unconstrained optimization problems nonmonotone line search nonmonotone trust region method global convergence superlinear convergence

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A modified approach to cross entropy method: Elitist stepped distribution algorithm

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APPLIED SOFT COMPUTING 2017年 58卷 756-769页

作者： Altun, Murat Pekcan, Onur Middle East Tech Univ Civil Engn Dept Ankara Turkey

This paper introduces a new algorithm named Elitist Stepped Distribution Algorithm (ESDA), which is inspired from the existing Cross Entropy Method (CEM) through the modification of elite sample based normal distribution used in CEM. Considering the natural behavior of normal distribution, ESDA is proposed to enhance the drawbacks of CEM through improving the efficiency in both exploration and exploitation processes when applying for complex function optimization problems. In ESDA, the elite sample percent defined in CEM is separated into two parts: (1) elite sample percent to calculate the mean value, and (2) elite sample percent to calculate standard deviation of normal distribution to construct an applicable balance between exploration and exploitation ability of the algorithm at a reasonable convergence speed. The elite sample percent parameter for the mean guides the algorithm to focus more on the better solutions and therefore improves the exploitation ability, whereas the elite sample percent parameter for the standard deviation controls the length of standard deviation to handle the exploration process more effectively. Performance of ESDA is investigated using unconstrained benchmark problems and compared with CEM, Simple Genetic Algorithm and Particle Swarm optimization. The comparisons on unimodal and multi-modal functions confirm the efficiency of the algorithm in both exploration and exploitation process. In addition, the performance of ESDA is tested using constrained engineering problems commonly used in literature by comparing its performance with the other ones statistically. The results on engineering problems also prove that ESDA is perfectly applicable in real-world applications. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Elitist stepped distribution algorithm Metaheuristic Cross entropy method optimization unconstrained optimization problems Engineering design problems

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Solving Multi-Objective problems Using Bird Swarm Algorithm

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IEEE ACCESS 2021年 9卷 36382-36398页

作者： Houssein, Essam H. Ahmed, Mohammed M. Abd Elaziz, Mohamed Ewees, Ahmed A. Ghoniem, Rania M. Menia Univ Fac Comp & Informat Al Minya 61519 Egypt Univ Sadat City Fac Comp & Artificial Intelligence Sadat City 32897 Egypt Zagazig Univ Fac Sci Zagazig 44511 Egypt Damietta Univ Dept Comp Dumyat 34511 Egypt Princess Nourah bint Abdulrahman Univ Dept Informat Technol Coll Comp & Informat Sci Riyadh 84428 Saudi Arabia Mansoura Univ Dept Comp Mansoura 35516 Egypt

This paper introduces an effective method by combining the multi-objective technique with the bird swarm algorithm (BSA) to obtain a new method called MBSA. The MBSA obtains some of the different non-dominated techniques that maintain variety amongst the optimal solutions. To verify and evaluate the effectiveness of the MBSA, collections of constrained, unconstrained, and engineering problems are measured. These problems have various Pareto front (PF) properties, including non-convex, convex, and discrete PFs. The results show that the MBSA has a good ability to obtain both a better solution spread and better convergence near the true PF. Furthermore, the quantitative and qualitative results indicate that the MBSA provides high convergence and good results in all experiments and with real-world problems against well-known algorithms in the literature.

关键词： optimization Birds Convergence Computers Transportation Sorting Search problems Bird swarm algorithm multi-objective optimization constrained optimization problems unconstrained optimization problems engineering optimization problems Pareto front Pareto optimal solutions

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A novel nature-inspired algorithm for optimization: Virus colony search

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ADVANCES IN ENGINEERING SOFTWARE 2016年 92卷 65-88页

作者： Li, Mu Dong Zhao, Hui Weng, Xing Wei Han, Tong Air Force Engn Univ Inst Aeronaut & Astronaut Engn Xian 710038 Shannxi Peoples R China

This paper presents a new powerful nature-inspired method named Virus Colony Search (VCS). VCS simulates diffusion and infection strategies for the host cells adopted by virus to survive and propagate in the cell environment. With the strategies, the individual in the new algorithm explores and exploits the search space more efficiently. To verify the performance of our VCS, both the unconstrained classic and CEC2014 modern benchmark functions, and constrained engineering design optimization problems are employed. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of VCS for global numerical and engineering optimization problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Nature-inspired algorithms optimization Virus colony search Survival strategy unconstrained optimization problems Engineering design problems

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A new conjugate gradient method based on Quasi-Newton equation for unconstrained optimization

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2019年 350卷 372-379页

作者： Li, Xiangli Shi, Juanjuan Dong, Xiaoliang Yu, Jianglan Guilin Univ Elect Technol Sch Math & Comp Sci Guangxi Key Lab Cryptog & Informat Secur Guilin 541004 Peoples R China North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Peoples R China

The spectral conjugate gradient method is an effective method for large-scale unconstrained optimization problems. In this paper, based on Quasi-Newton direction and Quasi Newton equation, a new spectral conjugate gradient method is proposed. This method is motivated by the three-term modified Polak-Ribiyre-Polyak (PRP) method and spectral parameters. The global convergence of algorithm is proved for general functions under a strong Wolfe line search. Numerical results show that the new algorithm is superior to the three-term modified PRP method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Spectral conjugate gradient method Quasi-Newton equation unconstrained optimization problems Global convergence

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Birds foraging search: a novel population-based algorithm for global optimization

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MEMETIC COMPUTING 2019年第3期11卷 221-250页

作者： Zhang, Zhuoran Huang, Changqiang Dong, Kangsheng Huang, Hanqiao Air Force Engn Univ Aeronaut & Astronaut Engn Coll Xian 710038 Shaanxi Peoples R China Northwestern Polytech Univ Unmanned Syst Res Inst Xian 710072 Shaanxi Peoples R China

Population-based algorithms have become a research hotspot for optimization problems and have been widely applied in various fields in recent decades. This paper presents the birds foraging search (BFS) algorithm, which is a novel population-based optimizer inspired by the different behaviors of birds during the foraging process for solving global optimization problems. The overall framework of the proposed algorithm involves three phases: the flying search behavior phase, the territorial behavior phase and the cognitive behavior phase. In the proposed algorithm, the first two phases balance the exploration and exploitation capabilities of the algorithm, and the third phase enhances the search efficiency. Classical benchmarks and CEC2014 benchmarks are employed to fully evaluate the performance of our BFS. The statistical results reveal that the BFS algorithm outperforms other conventional approaches and state-of-the art algorithms in terms of accuracy and convergence.

关键词： Population-based algorithm Global optimization Birds foraging search unconstrained optimization problems

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A note on teaching-learning-based optimization algorithm

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INFORMATION SCIENCES 2012年 212卷 79-93页

作者： Crepinsek, Matej Liu, Shih-Hsi Mernik, Luka Univ Maribor Fac Elect Engn & Comp Sci SLO-2000 Maribor Slovenia Calif State Univ Fresno Dept Comp Sci Fresno CA 93740 USA CALTECH Pasadena CA 91125 USA

Teaching-Learning-Based optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO's dominance. This paper reports our findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively. Our findings have revealed three important mistakes regarding TLBO: (1) at least one unreported but important step;(2) incorrect formulae on a number of fitness function evaluations;and (3) misconceptions about parameter-less control. Additionally, unfair experimental settings/conditions were used to conduct experimental comparisons (e.g., different stopping criteria). The experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy. The ultimate goal of this paper is to provide reminders for metaheuristics' researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects, and to allow fair comparisons of the TLBO algorithm to be made with other metaheuristic algorithms. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Teaching-learning-based optimization Constrained optimization problems unconstrained optimization problems Experimental replication

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Marathon runner algorithm: theory and application in mathematical, mechanical and structural optimization problems

MATERIALS TESTING

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MATERIALS TESTING 2024年第8期66卷 1267-1291页

作者： Mortazavi, Ali Izmir Democracy Univ Civil Engn Dept Izmir Turkiye

This study proposes a novel human-inspired metaheuristic search algorithm called marathon runner algorithm. This method mimics competitive behaviors observed in real marathon runners through mathematical modeling. Unlike classical elitist algorithms that prioritize position of the best agent, the marathon runner algorithm introduces a novel concept called vision point. This point considers the quality of the entire population, not just the leader. By guiding the population towards vision point, the risk of getting trapped in local optima is reduced. A two-part evaluation was conducted to thoroughly assess the search capabilities of the marathon runner algorithm. First, it is tested against a set of unconstrained benchmark mathematical functions and the algorithm's quantitative attributes, such as complexity, accuracy, stability, diversity, sensitivity, and convergence rate are analyzed. Subsequently, the algorithm was applied to mechanical and structural optimization problems with both continuous and discrete variables. This application demonstrated the effectiveness of the algorithm in solving practical engineering challenges with constraints. The outcomes are compared with those obtained by six other well-established techniques. The obtained results indicate that the marathon runner algorithm yields promising and competitive solutions for both mathematical, mechanical, and structural problems.

关键词： optimization techniques metaheuristic algorithms constrained optimization problems unconstrained optimization problems discrete and continuous variables

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