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

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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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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Experimental study and numerical prediction of the bond-slip law for concrete elements strengthened with FRP using metaheuristic algorithm

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CONSTRUCTION AND BUILDING MATERIALS 2024年 411卷

作者： Aghabagloo, Mehdi Carreras, Laura Barahona, Mario Barris, Cristina Baena, Marta Univ Girona Polytech Sch AMADE Girona 17003 Spain

Modeling an accurate bond-slip model between concrete and reinforcement is a significant challenge in fiber reinforced polymer (FRP) strengthened reinforced concrete (RC). This paper presents a novel method for developing and validating an accurate bond-slip model. By employing a numerical procedure based on the finite difference method and a metaheuristic optimization algorithm, this method eliminates the need for a specific bond-slip law shape. This unique feature enables the prediction of bond-slip laws for various strengthening methods, including EBR and NSM, as demonstrated in this study. Furthermore, the methodology allows for the prediction of bond-slip laws associated with premature failure. The numerical and experimental results obtained confirm the accuracy of the methodology.

关键词： Bond behavior metaheuristic algorithm CFRP strengthening Numerical modeling

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Plasma generation optimization: a new physically-based metaheuristic algorithm for solving constrained optimization problems

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ENGINEERING COMPUTATIONS 2021年第4期38卷 1554-1606页

作者： Kaveh, Ali Akbari, Hossein Hosseini, Seyed Milad Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

Purpose This paper aims to present a new physically inspired meta-heuristic algorithm, which is called Plasma Generation Optimization (PGO). To evaluate the performance and capability of the proposed method in comparison to other optimization methods, two sets of test problems consisting of 13 constrained benchmark functions and 6 benchmark trusses are investigated numerically. The results indicate that the performance of the proposed method is competitive with other considered state-of-the-art optimization methods. Design/methodology/approach In this paper, a new physically-based metaheuristic algorithm called plasma generation optimization (PGO) algorithm is developed for solving constrained optimization problems. PGO is a population-based optimizer inspired by the process of plasma generation. In the proposed algorithm, each agent is considered as an electron. Movement of electrons and changing their energy levels are based on simulating excitation, de-excitation and ionization processes occurring through the plasma generation. In the proposed PGO, the global optimum is obtained when plasma is generated with the highest degree of ionization. Findings A new physically-based metaheuristic algorithm called the PGO algorithm is developed that is inspired from the process of plasma generation. Originality/value The results indicate that the performance of the proposed method is competitive with other state-of-the-art methods.

关键词： metaheuristic algorithm Engineering problems Plasma generation optimization Truss structures Constrained benchmark functions

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Multi-satellite cooperative scheduling method for large-scale tasks based on hybrid graph neural network and metaheuristic algorithm

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ADVANCED ENGINEERING INFORMATICS 2024年 60卷

作者： Feng, Xiaoen Li, Yuqing Xu, Minqiang Harbin Inst Technol Deep Space Explorat Res Ctr Harbin 150001 Peoples R China

Traditional heuristic optimization algorithms are no longer applicable to the multiple satellites scheduling with large-scale tasks, as they are unable to provide satisfactory performance in terms of convergence and speed when addressing complex constraint conflicts. Therefore, we propose a multi-satellite cooperative scheduling method for large-scale tasks based on a hybrid graph neural network (GNN) and metaheuristic algorithm. By using the representation and extraction capability of GNN for relations in graph, the features of large-scale tasks and their constraint relations were expressed, and the generalized knowledge was extracted. And combined with metaheuristic algorithm, the optimization framework for large-scale satellite task scheduling based knowledge and data was implemented. The method consists of a GNN pre-solver module for static constraint conflicts and a metaheuristic optimization module for dynamic constraint conflicts of satellite missions. In the GNN pre-solver module, the graph sample and aggregate network was used to learn and extract the common knowledge in the static task-conflict graph, and provided effective prior information for the metaheuristic optimization module. The quadratic unconstrained binary optimization loss function was designed with multiple influencing factors, to convert the discrete optimization function into a continuous function;a greedy threshold was also added to improve the task completion rate. Finally, numerical experimental results showed that the proposed method can achieve an efficient solution to the multi-satellite scheduling problem with tens of thousands tasks. Compared with the commonly used multi-satellite scheduling algorithms (the genetic algorithm (GA) and greedy-GA), the proposed method can obtain higher-quality solutions under the same conditions and greatly improve the computational efficiency of large-scale mission planning.

关键词： Earth observation satellites Large-scale satellite missions Multi-satellite scheduling Graph neural network metaheuristic algorithm

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A metaheuristic algorithm for model predictive control of the oil-cooled motor in hybrid electric vehicles

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ENERGY 2024年 295卷

作者： Liu, Jiangchuan Ma, Qixin Zhang, Quanchang Hunan Univ Res Ctr Adv Powertrain Technol Dept Energy & Power Engn Changsha 410082 Peoples R China

The current energy management methods for hybrid vehicles are primarily focused on matching the power flow between the engine and the motor. In order to further reduce overall energy consumption and extend the vehicle ' s lifespan, this paper utilizes model predictive control (MPC) in the energy management of hybrid vehicles to control the energy consumption and temperature of the oil -cooled motor, with a focus on studying the algorithm for solving the optimization problem in MPC. The main work includes: Establishing a simplified model for the oil -cooled motor and training the working condition prediction model based on the worldwide harmonized light vehicles test cycle (WLTC) conditions utilizing long short-term memory (LSTM) recurrent neural network technology. Furthermore, proposing a novel metaheuristic algorithm based on the temporal nature of the problem, multiple hierarchical clustering algorithm (MHCA). In comparison with typical metaheuristic algorithms, the MPC optimized using MHCA exhibits significantly reduced computation time, with an average time of 0.1967s, indicating that using MHCA can theoretically achieve real-time control and eliminate lag in actual control processes. Furthermore, compared to other optimization algorithms, the MHCA algorithm demonstrates the lowest correlation between computation time and operational conditions, highlighting its high robustness in ensuring the safety of practical control.

关键词： Energy management Oil -cooled motor MPC LSTM metaheuristic algorithm

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Energy-efficient routing protocol for underwater wireless sensor networks using a hybrid metaheuristic algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartC期133卷

作者： Saemi, Behzad Goodarzian, Fariba Kavosh Inst Higher Educ Comp Dept Mahmood Abad Mazandaran Iran Heriot Watt Univ Edinburgh Business Sch EBS Riccarton EH14 4AS Currie Scotland Heriot Watt Univ Sch Social Sci Riccarton EH14 4AS Currie Scotland

Energy-efficient routing protocols for Underwater Wireless Sensor Networks (UWSNs) have become critical in recent years for the intelligent and reliable collection of data from the seas and oceans. UWSNs are a group of deep-water sensors that are used for marine exploration and ocean surveillance. This network is used to route data collected by sensors deployed at different water depths to surface water sensors (sinks). Transmitting the collected data from the ocean's depths to the surface is difficult due to the limited available bandwidth, inconvenient location, high mobility of the sensors, and, most importantly, limited energy. The purpose of this paper is to present a routing protocol that improves the reliability of data transmission from a source sensor to a destination sensor. A hybrid metaheuristic algorithm called GSLS is proposed to use in this protocol, which combines a Global Search algorithm (GSA) and a Local Search algorithm (LSA). The proposed GSA is an algorithm inspired by several of the Genetic algorithm's (GAs) solution updating properties. The proposed LSA algorithm is an extension of the scattered search algorithm. The proposed GSA and LSA are combined in parallel to search the problem's space simultaneously to find an optimal path in an acceptable time. as a result, more problem area is examined, and the algorithm's run time to find the best route is reduced. Our simulation results emphasize the high quality of the path, the algorithm's low energy consumption, and the algorithm's high speed in comparison to the state-of-the-art.

关键词： Energy -efficient Underwater wireless sensor networks Routing metaheuristic algorithm Genetic algorithm

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Optimal Stochastic Process Optimizer: A New metaheuristic algorithm With Adaptive Exploration-Exploitation Property

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IEEE ACCESS 2021年 9卷 108640-108664页

作者： Xu, Jiahong Xu, Lihong Tongji Univ Dept Elect & Informat Engn Shanghai 201804 Peoples R China

metaheuristic algorithms are constructed to solve optimization problems, but they cannot solve all the problems with best solutions. This work proposes a novel self-adaptive metaheuristic optimization algorithm, named Optimal Stochastic Process Optimizer (OSPO), which can solve different kinds of optimization problems with promising performance. Specifically, OSPO regards the procedure of optimization as a realization of stochastic process, and with the help of Subjective Probability Distribution Function (SPDF) and Receding Sampling Strategy proposed in this paper, OSPO can control the exploration-exploitation property online by the adaptive modification of the parameters in SPDF. This adaptive exploration-exploitation property of OSPO contributes to dealing with different kinds of problems;thus, it makes OSPO have the potential to solve at least a vast majority of optimization problems. The proposed algorithm is first benchmarked on uni-modal, multi-modal and composite test functions both in low and high dimensions. The results are verified by comparative studies with seven well-performed metaheuristic algorithms. Then, 21 real-world optimization problems are used to further investigate the effectiveness of OSPO. The winners of CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS algorithm, sCMAgES algorithm, EnMODE algorithm and COLSHADE algorithm are used as four comparative algorithms in real-world optimization problems. The analysis of simulations demonstrates that OSPO is able to provide very competitive performance compared to the comparative meta-heuristics both in benchmark functions and in real-world optimization problems;thus, the potential of OSPO to solve at least a vast majority of optimization problems is verified. A corresponding MATLAB APP demo is available on https://***/JiahongXu123/***.

关键词： Search problems Heuristic algorithms Space exploration Stochastic processes Measurement Diversity methods Benchmark testing Optimization metaheuristic algorithm benchmark exploration exploitation

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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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