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Multi-area economic environmental dispatch using multi-objective squirrel search algorithm

EVOLVING SYSTEMS 2022年第2期13卷 183-199页

作者： Sakthivel, V. P. Sathya, P. D. Govt Coll Engn Dept Elect & Elect Engn Dharmapuri 636704 Tamil Nadu India Annamalai Univ Dept Elect & Commun Engn Fac Engn & Technol Chidambaram 608002 Tamil Nadu India

The main objective of multi-area economic/environmental dispatch (MAEED) problems is the effective management of electrical energy generation by minimizing the total fuel cost and emissions of a multi-area power generation units, while satisfying various system and operating constraints. This paper bestows a new swarm intelligence approach, multi-objective squirrel search algorithm (MOSSA) to solve the MAEED problem. In the suggested approach, an exterior elitist vault system, crowding distance and fuzzy clustering principles are acquainted to procure a uniformly spaced Pareto optimal front curve. In addition, the MAEED problem is unraveled by squirrel search algorithm based weighted sum approach (SSA-WSA) with price penalty factors, artificial bee colony (ABC) and exchange market algorithm (EMA). The adequacy of the suggested strategy is approved on 40 generating units with single and multi-area power systems. The numeric results exhibit that the suggested MOSSA strategy accomplished much preferred trade-off solutions over the SSA-WSA, ABC, EMA and different strategies surfaced in the literature. The result transmuted evinces the desirability of MOSSA to solve the MAEED problem.

关键词： Economic and environmental dispatch Fuzzy clustering mechanism Multi-area power dispatch Pareto optimal front squirrel search algorithm

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Fault detection based on squirrel search algorithm and support vector data description for industrial processes

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SOFT COMPUTING 2022年第24期26卷 13639-13650页

作者： Alejandro Navarro-Acosta, Jesus Garcia-Calvillo, Irma D. Resendiz-Flores, Edgar O. Univ Autonoma Coahuila Ctr Invest Matemat Aplicadas Unidad Saltillo Saltillo 25280 Coahuila Mexico Tecnol Nacl Mexico IT Saltillo Div Estudios Posgrad & Invest Blvd V Carranza 2400 Col Tecnol Saltillo 25280 Coahuila Mexico

This paper proposes a novel fault detection system by the combination of Support Vector Data Description and squirrel search algorithm. This approach is capable to deal with processes or machines where the number of fault observations is small or not even available for training phase. In this work the use of classic Support Vector Data Description as well as its fast version with two kernel functions is proposed. The experimental results showed that the proposed system exhibits suitable capabilities for fault detection in complex industrial processes such as the one presented in this research. Moreover, a nonparametric statistical analysis is also included in order to compare the considered strategies and enhance the efficiency of the presented approach. Finally, a comparison with genetic algorithm approach and the one-class classifier based on support vectors is carried out which shows that the proposed algorithm outperforms traditional techniques.

关键词： Support Vector Data Description squirrel search algorithm Fault detection One-class classification

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A Hybrid algorithm Based on squirrel search algorithm and Invasive Weed Optimization for Optimization

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IEEE ACCESS 2019年 7卷 105652-105668页

作者： Hu, Hongping Zhang, Linmei Bai, Yanping Wang, Peng Tan, Xiuhui North Univ China Sch Sci Dept Math Taiyuan 030051 Shanxi Peoples R China

This paper proposes a hybrid algorithm called ISSA based on the combination of squirrel search algorithm (SSA) proposed in 2019 and invasive weed optimization (IWO) proposed in 2006. About 36 benchmark functions are employed to test the performances of ISSA. Then, ISSA is combined with support vector machine (SVM) and deterministic maximum-likelihood (DML) algorithm, respectively, and the two corresponding models ISSA-SVM and ISSA-DML are established for performing the grade classifications of air quality and the direction of arrival (DOA) estimation of MEMS vector hydrophone, respectively. The results of 36 benchmark functions prove that the proposed ISSA is able to provide very competitive results in terms of the average values, the standard derivation, and the convergence curves. The average accuracy rate of classification of ISSA-SVM model is the best and reaches 87.91971%, and the DOA estimations of ISSA-DML have the least root mean square error (RMSE) and the closest to the actual angles. Therefore, it is concluded that the proposed ISSA is an effective algorithm for function optimizations and is suitable to be combined with other algorithms and machine learning for classification and estimation.

关键词： squirrel search algorithm invasive weed optimization support vector machine deterministic maximum-likelihood optimization

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A novel nature-inspired algorithm for optimization: squirrel search algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 44卷 148-175页

作者： Jain, Mohit Singh, Vijander Rani, Asha Univ Delhi Netaji Subhas Inst Technol Instrumentat & Control Engn Div Delhi 110078 India

This paper presents a novel nature-inspired optimization paradigm, named as squirrel search algorithm (SSA). This optimizer imitates the dynamic foraging behaviour of southern flying squirrels and their efficient way of locomotion known as gliding. Gliding is an effective mechanism used by small mammals for travelling long distances. The present work mathematically models this behaviour to realize the process of optimization. The efficiency of the proposed SSA is evaluated using statistical analysis, convergence rate analysis, Wilcoxon's test and ANOVA on classical as well as modern CEC 2014 benchmark functions. An extensive comparative study is carried out to exhibit the effectiveness of SSA over other well-known optimizers in terms of optimization accuracy and convergence rate. The proposed algorithm is implemented on a real-time Heat Flow Experiment to check its applicability and robustness. The results demonstrate that SSA provides more accurate solutions with high convergence rate as compared to other existing optimizers.

关键词： Nature-inspired algorithm Unconstrained optimization squirrel search algorithm

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An Improved squirrel search algorithm With Reproductive Behavior

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IEEE ACCESS 2020年 8卷 101118-101132页

作者： Zhang, Xuncai Zhao, Kai Wang, Lingfei Wang, Yanfeng Niu, Ying Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China Zhengzhou Univ Light Ind Coll Architecture Environm Engn Zhengzhou 450002 Peoples R China

The squirrel search algorithm (SSA) is a recently proposed nature-inspired algorithm based on the dynamic foraging and gliding behavior of squirrels. Because of its simplicity and stability, the squirrel algorithm has attracted increasing research interest. However, the lack of exploration ability of the SSA may lead to premature convergence to the local optimum. To overcome this disadvantage, an improved SSA with reproductive behavior (RSSA) is proposed to solve the numerical optimization problem. First, the reproductive behavior of the invasive weed algorithm (IWO) is introduced to the conventional SSA to generate offspring individuals, and these offspring individuals are scattered into the search space by Gaussian distribution to complete the location update. This method makes it possible for individuals with poor fitness to enter the next generation search, improving the exploration ability of the SSA. Second, an adaptive step strategy is proposed to adaptively adjust the search step of squirrels according to the distance between each squirrel and other family members. This strategy effectively balances the exploration and exploitation of the algorithm. Finally, the performance of the proposed RSSA algorithm is evaluated using Wilcoxon's test on unimodal, multimodal, fixed-dimensional multimodal and CEC 2014 benchmark functions. Experimental results and statistical tests show that RSSA has better performance in terms of convergence, accuracy, and search capability compared with other state-of-the-art algorithms.

关键词： Heuristic algorithms Vegetation Optimization Convergence Benchmark testing search problems Particle swarm optimization Nature-inspired squirrel search algorithm exploration and exploitation Wilcoxon's test CEC 2014 benchmark functions

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Dynamic Multiobjective squirrel search algorithm Based on Decomposition With Evolutionary Direction Prediction and Bidirectional Memory Populations

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IEEE ACCESS 2019年 7卷 115997-116013页

作者： Wang, Yanjiao Du, Tianlin Liu, Tingting Zhang, Lei Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China Yangtze Univ Sch Econ & Management Jingzhou 434023 Peoples R China Yangtze Univ Natl Demonstrat Ctr Expt Elect & Elect Educ Jingzhou 434023 Peoples R China

In order to improve the optimization effect of dynamic multiobjective problems (DMOPs), this paper proposes dynamic multiobjective squirrel search algorithm based on decomposition with evolutionary direction prediction and bidirectional memory populations (DMOISSA/D-P&M). To enhance the adaptability of the changing environments, DMOISSA/D-P&M assigns every individual a modification vector, a positive memory population, and a reverse memory population, all of them are updated in real-time with evolution. The modification vector is used to predict the evolutionary direction and the memory populations are used to retain the evolutionary information in historical environments. The predicted evolutionary direction and the memory individuals take part in the optimizing process in the new environment, which improves the convergence speed. To enhance the optimizing ability in every transient environment, DMOISSA/D-P&M designs two searching strategies for squirrel search algorithm (SSA), the improved SSA satisfies different requirements of the multiobjective evolutionary algorithm based on decomposition (MOEA/D) at different evolutionary stages, which improves the convergence and the distribution of the obtained Pareto front in each transient environment. The experimental results on test functions of DMOPs show that DMOISSA/D-P&M has much better convergence, better distribution, and greater capabilities on coping with environmental changes compared with other dynamic multiobjective optimization algorithms.

关键词： Bidirectional memory populations dynamic multiobjective optimization evolutionary direction prediction squirrel search algorithm

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A Discrete Multi-Objective squirrel search algorithm for Energy-Efficient Distributed Heterogeneous Permutation Flowshop with Variable Processing Speed

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Computers, Materials & Continua 2024年第10期81卷 1757-1787页

作者： Liang Zeng Ziyang Ding Junyang Shi Shanshan Wang School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China Xiangyang Industrial Institute of Hubei University of Technology Xiangyang441100China

In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in *** paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed(DHPFSP-VPS),considering both the minimum makespan and total energy consumption(TEC)as objectives.A discrete multi-objective squirrel search algorithm(DMSSA)is proposed to solve the *** makes four improvements based on the squirrel search ***,in terms of the population initialization strategy,four hybrid initialization methods targeting different objectives are proposed to enhance the quality of initial ***,enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm,making it more suitable for discrete scheduling ***,regarding the search strategy,six local searches are designed based on problem characteristics to enhance search ***,a dynamic predator strategy based on Q-learning is devised to effectively balance DMSSA’s capability for global exploration and local ***,two speed control energy-efficient strategies are designed to reduce *** comparative experiments are conducted in this paper to validate the effectiveness of the proposed *** results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem.

关键词： Distributed heterogeneous permutation flowshop problem squirrel search algorithm muli-objective optimization energy-efficient variable processing speed

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Multi-Objective squirrel search algorithm for Multi-Area Economic Environmental Dispatch With Multiple Fuels and Valve Point Effects

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IEEE ACCESS 2021年 9卷 3988-4007页

作者： Sakthivel, V. P. Goh, Hui Hwang Srikrishna, Subramanian Sathya, P. D. Abdul Rahim, Sharul Kamal Govt Coll Engn Dharmapuri Dept Elect & Elect Engn Dharmapuri 636704 India Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Annamalai Univ Dept Elect Engn Chidambaram 608002 India Annamalai Univ Dept Elect & Commun Engn Chidambaram 608002 India Univ Teknol Malaysia Wireless Commun Ctr Fac Engn Johor Baharu 81310 Malaysia

The essential goal of multi-area economic environmental dispatch (MAEED) is to determine the optimum power generation schedule of each unit and power transfer between the areas in order to minimize fuel costs and pollutant emissions, when the generation, power balance and tie-line limits are satisfied. This paper focuses on developing multi-objective squirrel search algorithm (MOSSA) to solve the MAEED problem, of which the goal is to simultaneously minimize the total fuel cost and emission considering valve point effects and multi-fuel options. The proposed MOSSA combines squirrel search algorithm along with Pareto-dominance theory to generate non-dominated solutions. It uses an external elitist depository mechanism with crowding distance sorting to preserve the distribution diversity of Pareto-optimal solutions as the evolution continues. In addition, a fuzzy decision maker is used to select the best compromised solution from the obtained Pareto frontiers. Furthermore, the MAEED problem is unraveled by squirrel search algorithm based weighted sum approach with price penalty factors, artificial bee colony and exchange market algorithm. Different case studies are performed on 10-unit with three-area system, 40-unit with four-area system and 140-unit real Korean power system considering valve point effects and multi-fuel options which testify the supremacy of the suggested approach. The comparisons with state-of-the-art approaches suggest that MOSSA can generate more competitive trade-off solutions for solving the MAEED problems.

关键词： Fuzzy decision maker multi-area economic and environmental dispatch multiple fuels Pareto optimal front squirrel search algorithm

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Efficient Task Scheduling in Cloud Computing: A Multiobjective Strategy Using Horse Herd-squirrel search algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2024年第1期2024卷

作者： Parthasaradi, V. Karunamurthy, A. Hussaian Basha, C. H. Senthilkumar, S. EGS Pillay Engn Coll Dept Elect & Commun Engn Nagapattinam 611002 Tamil Nadu India Sri Manakula Vinayagar Engn Coll Dept MCA Pondicherry 605107 India SR Univ Dept Elect & Elect Engn Hanumakonda 506371 Telangana India

Cloud computing (CC) is a technology that enables the delivery of IT services outside of the workplace. CC, on the other hand, has had several drawbacks. The task scheduling issue is taken as one of the important difficulties because a solid mapping between available resources and users' activities is essential to reduce the execution time of users' jobs (i.e., minimize makespan) and maximize resource utilization. Because the service provider must offer several customers' benefits at distinct times and from distinct locations, task scheduling is indeed a serious challenge in CC. As a result, in the CC environment, these operations must be scheduled in a more dynamic and timely manner. The objective is to provide an enhanced task scheduling algorithm for allocating the task of the user to different computing resources. The major aim of the research work is to reduce the cost and the execution time as well as to improve the resource utilization of the task scheduling problem using the improved support vector machine (ISVM) and the optimization concept. The novel algorithm used here merges two familiar algorithms as squirrel search algorithm (SSA) and the horse herd optimization algorithm (HOA) leading to a new hybrid metaheuristic algorithm called the horse herd-squirrel search algorithm (HO-SSA). The developed HO-SSA assists in introducing a multiobjective optimization for efficiently handling task scheduling issues in the cloud sector. The proposed HO-SSA method for the task scheduling in CC model in terms of cost is 22.22%, 15.73%, and 38.74% better than SSA, HOA, and TSA, respectively. Similarly, the proposed HO-SSA method for the task scheduling in CC model with respect to energy is 9.68%, 5.35%, and 22.50% superior to SSA, HOA, and TSA, respectively. The proposed method outperformed the existing methods like SSA, HOA, and TSA in terms of cost, energy, degree of imbalance, makespan, speedup, and efficiency.

关键词： cloud computing horse herd optimization multiobjective optimization squirrel search algorithm task scheduling

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Multi-objective squirrel search algorithm to solve economic environmental power dispatch problems

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第12期30卷 1-31页

作者： V. Ponnuvel, Sakthivel Murugesan, Suman P. Duraisamy, Sathya Govt Coll Engn Dept Elect & Elect Engn Dharmapuri 636704 Tamil Nadu India Annamalai Univ Fac Engn & Technol Dept Elect Engn Chidambaram India Annamalai Univ Fac Engn & Technol Dept Elect & Commun Engn Chidambaram India

Major challenge in the modern power system assessment is to address the issue of Combined Economic and Environmental Power Dispatch (CEEPD). The prime intention of CEEPD is to mitigate the total expense in the power generation process considering the environmental collision caused owing to the discharge of gaseous pollutants of fossil energy. This paper bestows a new swarm intelligence technique, squirrel search algorithm (SSA) to deal with multi-objective CEEPD problem in power systems. The SSA is devised from the foraging behavior of squirrels which is based on the dynamic jumping and gliding strategies. Multi-Objective SSA (MOSSA) approach employs the Pareto dominance and crowding distance concepts for finding the Pareto front solutions set. An external elitist depository mechanism is employed to preserve Pareto front solutions acquired during the optimization process. Then, an optimality based fuzzy decision maker is used to decide the best compromise solution. Furthermore, a renovate strategy and selection rules are utilized in the MOSSA approach to appropriately handle the CEEPD constraints. To access the practicability and effectiveness of the proposed MOSSA algorithm, it has been applied for 6, 10, and 40 units' power systems and compared with those of other state-of-the-art approaches in the literature. Moreover, the performance metrics are computed for all the test system to attest the preeminence of the proposed MOSSA approach.

关键词： economic emission dispatch multi-objective optimization Pareto front solution squirrel search algorithm valve point loading

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