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The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 80卷 20-34页

作者： Shadravan, S. Naji, H. R. Bardsiri, V. K. Islamic Azad Univ Kerman Branch Dept Comp Engn Kerman Iran Grad Univ Adv Technol Dept Comp Engn Kerman Iran

Nature-inspired optimization algorithms, especially swarm based algorithms (SAs), solve many scientific and engineering problems due to their flexibility and simplicity. These algorithms are applicable for optimization problems without structural modifications. This work presents a novel nature-inspired metaheuristic optimization algorithm, called SailFish Optimizer (SFO), which is inspired by a group of hunting sailfish. This method consists of two tips of populations, sailfish population for intensification of the search around the best so far and sardines population for diversification of the search space. The SFO algorithm is evaluated on 20 well-known unimodal and multimodal mathematical functions to test different characteristics of the algorithm. In addition, SFO is compared with the six state-of-art metaheuristic algorithms in low and high dimensions. It also indicates competitive results for improvement of exploration and exploitation phases, avoidance of local optima, and high speed convergence especially on large-scale global optimization. The SFO algorithm outperforms the best algorithms in the literature on the majority of the test functions and it shows the statistically significant difference among other algorithms. Moreover, the SFO algorithm shows significantly great results for non-convex, non-separable and scalable test functions. Eventually, the promising results on five real world optimization problems indicate that the SFO is applicable for problem solving with constrained and unknown search spaces.

关键词： Swarm optimization metaheuristic algorithm Sailfish optimization algorithm Constrained engineering optimization

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Adaptive spiral dynamics metaheuristic algorithm for global optimisation with application to modelling of a flexible system

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APPLIED MATHEMATICAL MODELLING 2016年第9-10期40卷 5442-5461页

作者： Nasir, A. N. K. Ismail, R. M. T. Raja Tokhi, M. O. Univ Malaysia Pahang Fac Elect & Elect Engn Pekan Pahang 26600 Malaysia Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

This paper presents a nature-inspired metaheuristic algorithm namely linear adaptive spiral dynamics algorithm (LASDA) and its application to modelling of a flexible system. The performance of spiral dynamics algorithm (SDA) is in general not satisfactory due to the incorporation of a single radius and single angular displacement values during the whole search process. LASDA is proposed as an improved version of SDA where the spiral radius and angular displacement are dynamically varied by employing novel mathematical equadon based on linear function, which establishes a relationship between fitness value, spiral radius and angular displacement. The proposed algorithm is tested with various types of multimodal and unimodal benchmark functions and its performance in terms of fitness accuracy is discussed. A linear parametric modelling approach is utilised with an autoregressive model with exogenous inputs (ARX) structure for a flexible system. The proposed algorithm is then used to optimise parameters of the ARX structure. The performances of the LASDA in terms of convergence speed to the optimum value, fitness accuracy, time domain and frequency-domain responses of acquired model is presented in comparison to SDA, BFA and IBFA. The results show that the proposed algorithm achieves better performance in finding an optimal solution for the benchmark functions as well as for the modelling of the flexible system. (C) 2016 Elsevier Inc. All rights reserved.

关键词： metaheuristic algorithm Adaptive spiral dynamics System identification and modelling Flexible system

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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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An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期42卷 4051-4063页

作者： Kalimuthu, Rajkumar Thomas, Brindha Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumaracoil Tamil Nadu India

In today's world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms.

关键词： metaheuristic algorithm Internet of Things cloud computing resource optimization scheduling algorithms

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PRESEP: Cluster Based metaheuristic algorithm for Energy-Efficient Wireless Sensor Network Application in Internet of Things

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期133卷 1243-1263页

作者： Sharma, Sarvesh Kumar Chawla, Mridul Deenbandhu Chhotu Ram Univ Sci & Technol ECED Murthal 131039 Haryana India

A metaheuristic approach based hybrid data routing algorithm is proposed in this paper for energy-efficient wireless sensor network (WSN) applications in Internet of Things (IoT). High speed generation of network demands more IoT enabled services and platforms. Swarm optimization in route finding is integrated with an energy-efficient heuristic method to obtain an improved data routing algorithm for heterogeneous WSN. Particle swarm optimized Residual Energy Stable Election Protocol (PRESEP) simulation yields prolonged network lifetime, and stable cluster formation with balanced energy utilization. PRESEP protocol minimizes the cluster head (CH) selection cycle as an outcome of reactive protocol benefits of easy global search of swarm optimization for sustainable data routing in energy-efficient cluster based heterogeneous WSN. Enhancing the CH selection of developed residual energy based model with meta heuristic optimization is achieved with the incremented round of operations for varied heterogeneity factor. Support for heterogeneous WSN with various network sizes is evaluated and it outperformed previous heterogeneous algorithm in terms of network lifetime, alive nodes, and reduced energy consumption with a minimum number of repeated CH selection processes.

关键词： metaheuristic algorithm Routing Energy-efficient Network lifetime Internet of Things

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Language Education Optimization: A New Human-Based metaheuristic algorithm for Solving Optimization Problems

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1527-1573页

作者： Pavel Trojovsky Mohammad Dehghani Eva Trojovská Eva Milkova Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic

In this paper,based on the concept of the NFL theorem,that there is no unique algorithm that has the best performance for all optimization problems,a new human-based metaheuristic algorithm called Language Education Optimization(LEO)is introduced,which is used to solve optimization *** is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and *** is mathematically modeled in three phases:(i)students selecting their teacher,(ii)students learning from each other,and(iii)individual practice,considering exploration in local search and exploitation in local *** performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal,multimodal types and the CEC 2017 test *** optimization results show that LEO,with its acceptable ability in exploration,exploitation,and maintaining a balance between them,has efficient performance in optimization applications and solution *** efficiency in optimization tasks is compared with ten well-known metaheuristic *** of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior andmore competitive in combating the compared *** implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications.

关键词： Optimization language education exploration exploitation metaheuristic algorithm

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An effective metaheuristic algorithm for flowshop scheduling with deteriorating jobs

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第7期30卷 2733-2742页

作者： Wang, Hongfeng Huang, Min Wang, Junwei Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China

The deterioration effect in flowshop scheduling has gained a growing concern from the community of operational research in recent years. However, all of existing studies focus on two- or three-machine flow shops. In this paper, a m-machine (m > 3) flowshop scheduling problem (FSSP) with deteriorating jobs is investigated and a novel metaheuristic algorithm called multi-verse optimizer (MVO) is employed to solve it. The MVO algorithm can accomplish the optimization process via exchanging objects of universes through white/black hole and wormhole tunnels. In the novel MVO algorithm, a new elitist selection scheme is designed to construct the effective white/black hole tunnels, whereas two different local search operators are hybridized and embedded to further enhance the exploitation capability. Experimental results indicate that the proposed algorithm can achieve the satisfactory performance in solving the investigated FSSP with deteriorating jobs.

关键词： Scheduling Flowshop scheduling Deterioration metaheuristic algorithm Multi-verse optimizer

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Multifrequency-based tension intelligent identification for cables with unknown end-restraints using a metaheuristic algorithm

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STRUCTURES 2023年 50卷 775-790页

作者： Wang, Xiaoming Zhao, Jianling Sun, Yuan Wang, Fan Li, Zhaohui Changan Univ Key Lab Transport Ind Bridge Detect Reinforcement Xian 710064 Shaanxi Peoples R China Changan Univ Sch Highway Xian 710064 Shaanxi Peoples R China China Railway Design Corp Tianjin 300142 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Hubei Peoples R China

Vibration frequency methods are widely used for cable tension identification;however, calibrated a priori knowledge of the flexural stiffness, axial stiffness and end-restraints of cables cannot always be obtained in advance, leading to the inabilities of these methods in the identification from theoretical or empirical formu-lations. In this study, a multifrequency-based intelligent multiparameter identification method is proposed for the tension identification of a cable equipped with a single sensor. The framework of the method establishes an optimization objective function with the theoretical and on-site frequencies based on a cable dynamic model considering the effects of inclination, sagging, flexural stiffness and end-restraints. The identification domain is obtained from empirical formulas and dimensionless characteristic parameters, and the model is solved using a finite difference method. An efficient metaheuristic algorithm is introduced as an engine for the simultaneous identification of the cable tension and the parameters supposed to be calibrated first. The accuracy and efficiency of the method are demonstrated through comparative studies consisting of algorithm tests, tension identification tests of laboratory rigid short cables and some stay-cables of an on-site bridge.

关键词： Cable Tension identification Frequencies metaheuristic algorithm Inverse eigenvalue problem

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Advances in Henry Gas Solubility Optimization: A Physics-Inspired metaheuristic algorithm With Its Variants and Applications

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IEEE ACCESS 2024年 12卷 26062-26095页

作者： El-Shorbagy, Mohammed A. Bouaouda, Anas Nabwey, Hossam A. Abualigah, Laith Hashim, Fatma A. Prince Sattam bin Abdulaziz Univ Coll Sci & Humanities Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Hassan II Univ Casablanca Fac Sci & Technol Mohammadia 28806 Morocco Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm 32511 Egypt Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk 71491 Saudi Arabia Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Isra Univ Fac Informat Technol Amman 11622 Jordan Yuan Ze Univ Coll Engn Taoyuan 32003 Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Helwan Univ Fac Engn Cairo 11795 Egypt

The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.

关键词： Heuristic algorithms metaheuristics Search problems Behavioral sciences Mathematical models Task analysis Space exploration Problem-solving Engineering - general Engineering problems global optimization Henry gas solubility optimization metaheuristic 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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