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Particle guided metaheuristic algorithm for global optimization and feature selection problems

EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Kwakye, Benjamin Danso Li, Yongjun Mohamed, Halima Habuba Baidoo, Evans Asenso, Theophilus Quachie Univ Sci & Technol China Sch Management Hefei 230026 Anhui Peoples R China Tech Univ Carolo Wilhelmina Braunschweig Inst High Frequency Tech Univ Pl 2 D-38106 Braunschweig Germany Univ Oslo Fac Med Domus Med Postbokx1122 Blindern N-0317 Oslo Norway

Optimization problems can be seen in numerous fields of practical studies. One area making waves in the application of optimization methods is data mining in machine learning. An important preprocessing technique of data mining where irrelevant variables are discarded from the datasets and holding onto variables with important information is referred to as feature selection (FS). FS is critical to tackling the 'curse of dimensionality' by reducing the number of features, minimizing computational expensiveness and maximizing the accuracy of the machine learning models. Swarm Intelligence (SI)-based meta-heuristic algorithms (MAs) have been widely employed to solve several optimization problems like FS. However, common drawbacks identified with these algorithms include getting trapped in local optima, especially in situations where the search space is large (high dimensional space). This study proposes a new hybrid SI-based MA called Particle Swarm-guided Bald Eagle Search (PS-BES). The algorithm utilizes the speed of Particle Swarm to guide Bald Eagles in their search to ensure a smooth transition of the algorithm from exploration to exploitation. Additionally, we introduce the Attack-Retreat-Surrender technique, a new local-optima escape technique to enhance the balance between diversification and intensification of PS-BES. To establish the outstanding performance of the proposed algorithm, PS-BES is comprehensively analyzed utilizing 26 Benchmark functions. Further, the practicality of PS-BES is highlighted by its binary version for feature selection and evaluated using 27 classification datasets from the UCI repository. The results prove the overall superiority of PS-BES and bPS-BES as opposed the 10 state-of-the-art algorithms employed in the study.

关键词： Feature selection Global optimization metaheuristic algorithm Particle swarm optimization Bald eagle search Hybrid algorithm

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Levy Arithmetic algorithm: An enhanced metaheuristic algorithm and its application to engineering optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 241卷

作者： Barua, Sujoy Merabet, Adel St Marys Univ Div Engn Halifax NS Canada St Marys Univ Div Engn 923 Robie St Halifax NS B3H 3C3 Canada

Levy Arithmetic algorithm is an upgraded metaheuristic optimization approach proposed to enhance the Arithmetic Optimization algorithm using the Levy random step. Arithmetic Optimization algorithm addresses diverse optimization problems by employing arithmetic operators. However, its linear search capability might hinder attaining optimal solutions, which leads to stagnation. In this study, the Levy Arithmetic algorithm is introduced by combining the Arithmetic Optimization algorithm and the Levy random step to enhance search capabilities and minimize computational demands for improved outcomes. The evaluation encompassed ten CEC2019 benchmark functions, four established real-world engineering problems, and the Economic Load Dispatch of microgrids with renewable energy integration. Using evaluation metrics such as standard deviation and mean, along with hypothesis tests, the study conducts a thorough comparison between the performance of the proposed algorithm and that of the conventional Arithmetic Optimization algorithm. The results show that the Levy Arithmetic algorithm achieves optimization with a minimized number of evaluations in terms of standard deviation and mean, compared to the Arithmetic Optimization algorithm. Additionally, the proposed method was compared with various well-known and recent metaheuristics algorithms, and the Levy Arithmetic algorithm consistently demonstrates superior performance, especially in contrast to the Arithmetic Optimization algorithm.

关键词： Arithmetic Optimization algorithm Levy Arithmetic algorithm metaheuristic algorithm CEC Benchmarks Engineering Problems Microgrid ELD

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Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization

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KNOWLEDGE-BASED SYSTEMS 2024年 295卷

作者： Wang, Xiaopeng Snasel, Vaclav Mirjalili, Seyedali Pan, Jeng-Shyang Kong, Lingping Shehadeh, Hisham A. VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70800 Czech Republic Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane 4006 Australia Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Amman Arab Univ Coll Comp Sci & Informat Amman 11953 Jordan

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimization processes of metaheuristic algorithms. The performance of the APO was verified via experimental simulations and compared with 32 state-of-the-art algorithms. Wilcoxon signed-rank test was performed for pairwise comparisons of the proposed APO with the state-of-the-art algorithms, and Friedman test was used for multiple comparisons. First, the APO was tested using 12 functions of the 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, the proposed APO was used to solve five popular engineering design problems in a continuous space with constraints. Moreover, the APO was applied to solve a multilevel image segmentation task in a discrete space with constraints. The experiments confirmed that the APO could provide highly competitive results for optimization problems. The source codes of Artificial Protozoa Optimizer are publicly available at https://***/projects and https://***/matlabcentral/ fileexchange/162656-artificial-protozoa-optimizer.

关键词： metaheuristic algorithm Artificial protozoa optimizer Constrained optimization Engineering design Image segmentation

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A reinforcement learning-based metaheuristic algorithm for solving global optimization problems

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ADVANCES IN ENGINEERING SOFTWARE 2023年第1期178卷

作者： Seyyedabbasi, Amir Istinye Univ Fac Engn & Nat Sci Software Engn Dept Istanbul Turkiye

The purpose of this study is to utilize reinforcement learning in order to improve the performance of the Sand Cat Swarm Optimization algorithm (SCSO). In this paper, we propose a novel algorithm for the solution of global optimization problems that is called RLSCSO. In this method, metaheuristic algorithm is combined with rein-forcement learning techniques to form a hybrid metaheuristic algorithm. This study aims to provide search agents with the opportunity to perform efficient exploration of the search space in order to find a global optimal solution by using efficient exploration and exploitation to find optimal solutions within a given search space. A comprehensive evaluation of the RLSCSO has been conducted on 20 benchmark functions and 100-digit chal-lenge basic test functions. Additionally, the proposed algorithm is applied to the problem of localizing mobile sensor nodes, which is NP-hard (nondeterministic polynomial time). Several extensive analyses have been conducted in order to determine the effectiveness and efficiency of the proposed algorithm in solving global optimization problems. In terms of cost values, the RLSCSO algorithm provides the optimal solution, along with tradeoffs between exploration and exploitation.

关键词： metaheuristic algorithm Reinforcement learning algorithm Sand cat swarm optimization Q-learning Machine learning

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An inverse reinforcement learning framework with the Q-learning mechanism for the metaheuristic algorithm

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KNOWLEDGE-BASED SYSTEMS 2023年第1期265卷

作者： Zhao, Fuqing Wang, Qiaoyun Wang, Ling Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China

A reward function is learned from the expert examples by inverse reinforcement learning (IRL), which is more reliable than an artificial method. The moth-flame optimization algorithm (MFO), which is based on the navigation mechanism of a moth flying at night, has been extensively employed to address the complex optimization problem. An inverse reinforcement learning framework with the Q-learning mechanism (IRLMFO) is designed to strengthen the performance of the MFO algorithm in a large-scale real-parameter optimization problem. The right strategy is chosen by the Q-learning mechanism, using historical data provided by the relevant approach in the strategy pool, which stores strategies that include diverse functions. The competition mechanism is designed to strengthen the exploitation capability of the IRLMFO algorithm. The performance of the IRLMFO is verified on the benchmark test suite in CEC 2017. Experimental results illustrate that the IRLMFO outperforms state-of-the-art algorithms. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Inverse reinforcement learning (IRL) Q-learning metaheuristic algorithm Moth-flame optimization algorithm (MFO) Competition mechanism

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Drops on surface optimization (DSO): A new reinforcement learning based metaheuristic algorithm for virtual network functions placement in distributed cloud architectures

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COMPUTER NETWORKS 2022年 219卷

作者： Mohammadi, Marzieh Motamedi, S. Ahmad Sharifian, Saeed Amirkabir Univ Technol Fac Elect Engn Tehran Iran

With the goal of making the networks more agile, flexible and cost efficient, network function virtualization (NFV) is one of the latest and most promising technologies introduced. This technology is the key block of future internet and plays a critical role in modern networks. Using this paradigm, different network functions can be independently deployed on general purpose hardware instead of residing on dedicated hardware (network appliances in traditional networks). NFV enables faster development and lower costs. As any other new technology, NFV has some emerging challenges along with its benefits. The main research challenge of NFV is the necessity of optimally placing the virtual network functions (VNFs) in an NFV-enabled network. This problem is mainly known as the VNF placement (VNFP) problem in the literature. Due to the nature of network applications, this placement should be scalable for large network sizes. In this paper, the VNFP problem is addressed and a new metaheuristic algorithm is proposed based on the behavior of droplets on a surface, named the Drops on Surface Optimization (DSO) algorithm. In order to enhance the proposed algorithm by dynamic selection strategy, a reinforcement learning approach (Q learning) is adopted. A comprehensive evaluation is carried out using standard networks, compared with several methods. Simulation results reveal that our proposed algorithm outperforms other state-of-the-art approaches in terms of total end-to-end propagation delay of the placed service chain, cost of placement, number of active servers involved in the placement and scalability.

关键词： Cloud architecture metaheuristic algorithm Network function virtualization (NFV) Optimization Reinforcement learning Resource allocation

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Artificial coronary circulation system: A new bio-inspired metaheuristic algorithm

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SCIENTIA IRANICA 2019年第5期26卷 2731-2747页

作者： Kaveh, A. Kooshkebaghi, M. Iran Univ Sci & Technol Sch Civil Engn Tehran 16 Iran Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran

A new swarm intelligence optimization technique, called Artificial Coronary Circulation System (ACCS), is proposed in this study. This optimization method simulates the growth of coronary arteries (veins) in the human heart. In this algorithm, each capillary is considered a candidate solution. This algorithm starts with a random initial population of candidate solutions and evaluates them by using Coronary Growth Factor (CGF). In each run, the best candidate solution is selected as the main coronary vessel (artery or vein), and the other capillaries are considered as searchers of the search space. Then, the heart decides whether other candidates move toward/away from the main coronary vessels and searches for the optimal solution by using heart memory. Finally, the application of the proposed algorithm is demonstrated with respect to some benchmark functions and some mechanical problems, confirming the potential and capability of the new algorithm. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： metaheuristic algorithm Optimization Artificial coronary circulation system Coronary arteries growth Human heart arterial tree

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Optimal design of the neutron multiplicity counter based on metaheuristic algorithm

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FRONTIERS IN PHYSICS 2023年 11卷

作者： Lu, Yunfeng Zhang, Quanhu Yao, Qingxu Wang, Yu Xian Res Inst High Technol Xian Shaanxi Peoples R China

The neutron multiplicity measurement and analysis method is a very important measurement and analysis method in the field of international arms control verification and nuclear safeguards, and its measurement accuracy depends to some extent on detector performance. In this paper, several newer current metaheuristic algorithms are introduced to achieve neutron multiplicity counter layout optimization by combining with MC (Monte Carlo) simulation software. The algorithm with the best finding ability is selected for the optimization of another detector layout. Based on the optimization results, k-means clustering analysis is introduced so that the optimized structure can be applied to neutron multiplicity measurements. The results show that the metaheuristic algorithm has good application capability for neutron detector layout optimization and can be further investigated.

关键词： neutron multiplicity detector layout optimization cluster analysis MC simulation metaheuristic algorithm

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Design-Optimization of Conventional Steel Structures for Realization of the Sustainable Development Objectives Using metaheuristic algorithm

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BUILDINGS 2024年第7期14卷 2028页

作者： Negarestani, Mohammad Nader Hajikandi, Hooman Fatehi-Nobarian, Bahador Sardroud, Javad Majrouhi Islamic Azad Univ Dept Civil Engn Kish Int Branch Kish Isl *** Iran Islamic Azad Univ Cent Tehran Branch Dept Civil Engn Tehran *** Iran Islamic Azad Univ Dept Civil Engn Aras Branch Jolfa *** Iran

The construction industry presents a significant environmental challenge due to its substantial environmental footprint, utilization of limited natural resources, and contribution to pollution and climate change. Additionally, optimizing the weight, cost, and duration of construction is crucial for enhancing serviceability, flexibility, efficiency, and profitability. In this research, the relationship between structure weight and other objective functions was explored using the single-objective gray wolf algorithm to investigate their impact on carbon footprint, water footprint, and construction time. Furthermore, employing a multi-objective optimization algorithm, a building structure was optimized for three systems featuring different structural frames based on the specified objective functions. The results revealed that the structure with intermediate steel moment-resisting frames exhibited the shortest construction time but incurred the highest construction cost. Conversely, the structure with intermediate steel moment-resisting frames with special steel concentric bracing demonstrated the lowest carbon footprint and water footprint among the studied structural frames. Consequently, the structure with intermediate steel moment-resisting frames with special concentric steel bracing was proposed as a green structure, emphasizing its environmentally friendly characteristics.

关键词： green structure water footprint carbon footprint metaheuristic algorithm

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Walrus optimizer: A novel nature-inspired metaheuristic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Han, Muxuan Du, Zunfeng Yuen, Kum Fai Zhu, Haitao Li, Yancang Yuan, Qiuyu Tianjin Univ Sch Civil Engn State Key Lab Hydraul Engn Intelligent Construct & Tianjin 300354 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore Hebei Univ Engn Coll Civil Engn Handan 056038 Peoples R China

metaheuristic algorithms are intelligent optimization approaches that lead the searching procedure through utilizing exploitation and exploration. The increasing complexity of real-world optimization problem has prompted the development of more metaheuristic algorithms. Hence, this work proposes a novel swarm intelligence algorithm, Walrus optimizer (WO). It is inspired by the behaviors of walruses that choose to migrate, breed, roost, feed, gather and escape by receiving key signals (danger signals and safety signals). To test the capability of the proposed algorithm, 23 standard functions and the benchmark suite from the IEEE (Institute of Electrical and Electronics Engineers) Congress on Evolutionary Computation (CEC) 2021 are used. In addition, to evaluate the practicability of the proposed algorithm to solve various real-world optimization problems, 6 standard classical engineering optimization problems are examined and compared. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the computation time of the program, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Friedman and Wilcoxon analysis. The results demonstrate that the proposed algorithm can provide special stability features and very competitive performance in dealing with high-dimensional benchmarks and real-world problems. The proposal of WO promotes the continuous development and application expansion of artificial intelligence, improves the efficiency of optimization calculation, and provides powerful tools for solving complex problems in the real world. The source code of WO is publicly availabe at https://***/matlabcentral/fileexchange/154702-walrus-optimizer-wo.

关键词： Walrus Optimizer (WO) metaheuristic algorithm Swarm intelligence Exploration Exploitation

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