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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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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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metaheuristic algorithm for energy efficient clustering scheme in wireless sensor networks

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MICROPROCESSORS AND MICROSYSTEMS 2023年第1期101卷

作者： Prakash, Ved Pandey, Suman AKTU KNIT Sultanpur Lucknow India

The utilization of wireless sensor networks (WSNs), which combine sensing, communication, and computation in an intelligent environment. The use of energy becomes the primary obstacle for sensor nodes to overcome. Clustering and routing algorithms are promising ways to fix the issue and prolong the network's effectiveness. The availability of restricted energy resources is the most significant challenge in WSNs. In this paper, we propose an energy-efficient clustering scheme integrated with a particle swarm optimization method called PSO-EECS for wireless sensor networks to raise energy efficiency while simultaneously increasing the lifetime of sensor nodes. The predominant concern being addressed in this paper is the selection of a Cluster Head (CH), which helps gather, aggregate and forward the data from the cluster-based routing paradigm. PSO-EECS algo-rithm is used to optimize the fitness parameters for CH selection, which include the energy ratio, distance considerations, node density, load balancing, and the Network's average energy, among other things. The simulation results reveal that the proposed technique offers a considerable improvement in network stability and operation time compared to the currently used approaches. Compared to the PSOGSA, GAPSO-H, and ABE-DE protocol, simulation findings demonstrate that PSO-EECS enhances stability by 95.4%, 34.8%, and 29.2%, respectively.

关键词： WSNs PSO-EECS Clustering scheme metaheuristic algorithm Cluster head

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Review of Nature Inspired metaheuristic algorithm Selection for Combinatorial t-Way Testing

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IEEE ACCESS 2022年 10卷 27404-27431页

作者： Muazu, Aminu Aminu Hashim, Ahmad Sobri Sarlan, Aliza Univ Teknol PETRONAS Fac Sci & Informat Technol Comp & Informat Sci Dept Seri Iskandar 32610 Perak Malaysia Umaru Musa Yaradua Univ Fac Nat & Appl Sci Comp Sci Dept Katsina 820102 Nigeria

The metaheuristic algorithm is a very important area of research that continuously improves in solving optimization problems. Nature-inspired is one of the metaheuristic algorithm classifications that has grown in popularity among researchers over the last few decades. Nature-inspired metaheuristic algorithms contribute significantly to tackling many standing complex problems (such as the combinatorial t-way testing problem) and achieving optimal results. One challenge in this area is the combinatorial explosion problem, which is always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation has been selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-Time Hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization, whilst others were not. This research paper outlines a hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021), such as the Coronavirus Optimization algorithm, Ebola Optimization algorithm, Harmony Search, Tiki-Taka algorithm, and so on. The purpose of this review is to revisit and carry out an up-to-date review of these distinguished algorithms with their respective current states of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics have a simple structure that can be adopted in different areas to become more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters settings to achieve an optimal solution.

关键词： metaheuristics Testing Heuristic algorithms Explosions Software algorithms Licenses Genetic algorithms metaheuristic algorithm nature inspired combinatorial t-way testing test case optimization

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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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metaheuristic algorithm-Based Vibration Response Model for a Gas Microturbine

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SENSORS 2022年第12期22卷 4317-4317页

作者： Montoya-Santiyanes, L. A. Rodriguez-Abreo, Omar Rodriguez, Eloy E. Rodriguez-Resendiz, Juvenal Univ Politecn Queretaro Ind Technol Div Carretera Estatal 420 El Marques 76240 Mexico Red Invest OAC Optimizac Automatizac & Control El Marques 76240 Mexico Ctr Ingn & Desarrollo Ind CIDESI Queretaro 76125 Mexico Univ Autonoma Queretaro Fac Ingn Queretaro 76010 Mexico

Data acquisition and processing are areas of research in fault diagnosis in rotating machinery, where the rotor is a fundamental component that benefits from dynamic analysis. Several intelligent algorithms have been used to optimize investigations of this nature. However, the Jaya algorithm has only been applied in a few instances. In this study, measurements of the amplitude of vibration in the radial direction in a gas microturbine were analyzed using different rotational frequency and temperature levels. A response surface model was generated using a polynomial tuned by the Jaya metaheuristic algorithm applied to the averages of the measurements, and another on the whole sample, to determine the optimal operating conditions and the effects that temperature produces on vibrations. Several tests with different orders of the polynomial were carried out. The fifth-order polynomial performed better in terms of MSE. The response surfaces were presented fitting the measured points. The roots of the MSE, as a percentage, for the 8-point and 80-point fittings were 3.12% and 10.69%, respectively. The best operating conditions were found at low and high rotational frequencies and at a temperature of 300 degrees C. High temperature conditions produced more variability in the measurements and caused the minimum value of the vibration amplitude to change in terms of rotational frequency. Where it is feasible to undertake experiments with minimal variations, the model that uses only the averages can be used. Future work will examine the use of different error functions which cannot be conveniently implemented in a common second-order model. The proposed method does not require in-depth mathematical analysis or high computational capabilities.

关键词： microturbine vibration analysis metaheuristic algorithm response surface

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Flood algorithm: a novel metaheuristic algorithm for optimization problems

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2278-e2278页

作者： Ozkan, Ramazan Samli, Ruya Natl Def Univ Dept Comp Engn Istanbul Turkiye Istanbul Univ Cerrahpasa Dept Comp Engn Istanbul Turkiye

metaheuristic algorithms are an important area of research that provides significant advances in solving complex optimization problems within acceptable time periods. Since the performances of these algorithms vary for different types of problems, many studies have been and need to be done to propose different metaheuristic algorithms. In this article, a new metaheuristic algorithm called flood algorithm (FA) is proposed for optimization problems. It is inspired by the flow of flood water on the earth's surface. The proposed algorithm is tested both on benchmark functions and on a real-world problem of preparing an exam seating plan, and the results are compared with different metaheuristic algorithms. The comparison results show that the proposed algorithm has competitive performance with other metaheuristic algorithms used in the comparison in terms of solution accuracy and time.

关键词： metaheuristic algorithm Flood algorithm Optimization Exam seat planning

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Particle guided metaheuristic algorithm for global optimization and feature selection problems

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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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