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

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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Adaptive mutation quantum-inspired squirrel search algorithm for global optimization problems

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ALEXANDRIA ENGINEERING JOURNAL 2022年第9期61卷 7441-7476页

作者： Zhang, Yanan Wei, Chunwu Zhao, Juanjuan Qiang, Yan Wu, Wei Hao, Zifan Taiyuan Univ Technol Coll Informat & Comp Daxue St 209 Jinzhong 030600 Shanxi Peoples R China Shanxi Prov Peoples Hosp Shuangtasi 29 Taiyuan 030012 Shanxi Peoples R China

This paper presents a novel Adaptive Mutation Quantum-inspired squirrel search algorithm (AM-QSSA). Firstly, based on the population mutation, a location-update of quantum state correlative and attractor method is proposed. By introducing a random process to modify the sliding factor of the local attractor, the inevitable lack of diversity in the population renewal method is solved. The premature convergence problem of adaptive mutation rate improvement algorithm based on squirrel position update mode is introduced. Meanwhile, the paper decomposes the location update process of the SSA, and improves it with quantum-behavior. Furthermore, it proposes a novel quantum-inspired squirrels search algorithm. This method finds the complementary effect between quantum behavior and squirrel search algorithm, and solves the problem of premature convergence probability of SSA. In addition, it improves population diversity, and achieves the balance between global and local search. The efficiency of the proposed AM-QSSA is evaluated using exploitation analysis, exploration analysis, success rate analysis, convergence rate analysis on classical benchmark functions as well as Congress on Evolutionary Computation (CEC) 2017 test functions. For further study, AM-QSSA optimizes an image registration problem for an extensive study to check its applicability. The results reveal that AM-QSSA is more efficient and stable than SSA. And it is comparable to the most advanced optimization algorithms.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Metaheuristic method squirrel search algorithm (SSA) Quantum theory Unconstrained optimization Self-adaptive mutation

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The optimal multi-degree reduction of Ball Bezier curves using an improved squirrel search algorithm

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ENGINEERING WITH COMPUTERS 2023年第2期39卷 1143-1166页

作者： Cao, Huanxin Zheng, Hongchan Hu, Gang Northwestern Polytech Univ Sch Math & Stat Xian 710072 Shaanxi Peoples R China Xian Univ Technol Dept Appl Math Xian 710054 Shaanxi Peoples R China

As a new nature-inspired swarm intelligence optimizer, squirrel search algorithm (SSA) has shown potential to solve several real-world problems, but for some complex problems, it still suffers from degraded performance. In this paper, a hybrid squirrel search algorithm (NOSSA) combined with optimal neighborhood update and quasi-opposition learning strategies is proposed to overcome the drawback of population update guided only by leading individuals in SSA. NOSSA adopts a stochastic optimal neighborhood update strategy to improve convergence speed and accuracy, and incorporates a Quasi-opposition learning strategy to enhance exploration. To verify its efficiency, NOSSA has been tested on 23 classic benchmark functions. Experimental results show that NOSSA has better performance on search-efficiency, convergence rate and solution accuracy compared with the representative stochastic optimizers. Furthermore, intelligent algorithms are introduced into the optimal multi-degree reduction of Ball Bezier curves and two new methods are proposed for the multi-degree reduction of center curve and radius function of Ball Bezier curve respectively. Experimental results demonstrate the effectiveness of the methods and show that NOSSA performs best among the representative stochastic optimizers in the degree reduction. The methods achieve the automatic and intelligent degree reduction of Ball Bezier curves.

关键词： squirrel search algorithm Optimal neighborhood Quasi-opposition learning Ball Bezier curve Multi-degree reduction

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A Multi-objective Improved squirrel search algorithm based on Decomposition with External Population and Adaptive Weight Vectors Adjustment

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 542卷 123526-123526页

作者： Wang, Yanjiao Du, Tianlin Northeast Elect Power Univ Sch Elect Engn Changchun Rd Jilin 132012 Jilin Peoples R China

In order to further improve the performance of the Multi-objective Evolutionary algorithm Based on Decomposition (MOEA/D) in solving multi-objective optimization problems, this paper constructs a multi-objective optimization algorithm by taking MOEA/D as the multi-objective framework and the squirrel search algorithm (SSA) as the core evolutionary strategy. Besides, both of them are modified and the Multi-objective Improved squirrel search algorithm based on Decomposition with External Population and Adaptive Weight Vectors Adjustment (MOEA/D-EWA-ISSA) is proposed. MOEA/D-EWA-ISSA establishes an external population for every individual to retain the evolutionary information and maintain the population diversity, external individuals participate in evolution and produce better offspring, the convergence and distribution of Pareto Front (PF) are improved. As for SSA, the jumping search method and the progressive search method are introduced to it, different evolutionary strategies are provided to solve subproblems, which further improves the ability of core evolutionary strategy to solve subproblems and the convergence of the obtained PF. Furthermore, MOEA/D-EWA-ISSA adjusts every weight vector adaptively according to the population's actual evolutionary direction and the representative neighbor weight vectors, the distribution of the obtained PF is improved as well. The experimental results on multi-objective test functions show that the convergence and distribution of PF have obvious improvement when the improved SSA, the improved MOEA/D and the whole MOEA/D-EWA-ISSA are used to solve multi-objective optimization problems. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Multi-objective optimization Decomposition squirrel search algorithm Adaptive weight vectors adjustment

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Single and multi-area multi-fuel economic dispatch using a fuzzified squirrel search algorithm

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Protection and Control of Modern Power Systems 2021年第1期6卷 147-159页

作者： V.Ponnuvel Sakthivel P.Duraisamy Sathya Department of Electrical and Electronics Engineering Government College of EngineeringDharmapuriTamilnadu636704India Department of Electronics and Communication Engineering FEATAnnamalai UniversityChidambaramTamilnadu608002India

Multi-Area Multi-Fuel Economic Dispatch (MAMFED) aims to allocate the best generation schedule in each area and to offer the best power transfers between different areas by minimizing the objective functions among the available fuel alternatives for each unit while satisfying various constraints in power systems. In this paper, a Fuzzified squirrel search algorithm (FSSA) algorithm is proposed to solve the single-area multi-fuel economic dispatch (SAMFED) and MAMFED problems. squirrel search algorithm (SSA) mimics the foraging behavior of squirrels based on the dynamic jumping and gliding strategies. In the SSA approach, predator presence behavior and a seasonal monitoring condition are employed to increase the search ability of the algorithm, and to balance the exploitation and exploration. The suggested approach considers the line losses, valve point loading impacts, multi-fuel alternatives, and tie-line limits of the power system. Because of the contradicting nature of fuel cost and pollutant emission objectives, weighted sum approach and price penalty factor are used to transfer the bi-objective function into a single objective function. Furthermore, a fuzzy decision strategy is introduced to find one of the Pareto optimal fronts as the best compromised solution. The feasibility of the FSSA is tested on a three-area test system for both the SAMFED and MAMFED problems. The results of FSSA approach are compared with other heuristic approaches in the literature. Multi-objective performance indicators such as generational distance, spacing metric and ratio of non-dominated individuals are evaluated to validate the effectiveness of FSSA. The results divulge that the FSSA is a promising approach to solve the SAMFED and MAMFED problems while providing a better compromise solution in comparison with other heuristic approaches.

关键词： Fuzzy set theory Heuristic optimization Multi-area economic dispatch Pareto-optimal front squirrel search algorithm Tie-line constraint

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A Robust Video Watermarking Scheme with squirrel search algorithm

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Computers, Materials & Continua 2022年第5期71卷 3069-3089页

作者： Aman Bhaskar Chirag Sharma Khalid Mohiuddin Aman Singh Osman A.Nasr Mamdooh Alwetaishi Computer Science and Engineering Lovely Professional UniversityPunjab144411India Department of Management Information Systems King Khalid UniversityGuraigerAbha62529Saudi Arabia Department of Civil Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia

Advancement in multimedia technology has resulted in protection against distortion,modification,and *** implementing such protection,we have an existing technique called watermarking but obtaining desired distortion level with sufficient robustness is a challenging task for watermarking in multimedia *** the paper,we proposed a smart technique for video watermarking associating meta-heuristic algorithms along with an embedding method to gain an optimized *** main aim of the optimization algorithm is to obtain solutions with maximum robustness,and which should not exceed the set threshold of *** represent the accuracy of the proposed scheme,we employ a popular video watermarking technique(DCT domain)having frame selection and embedding method for watermarking.A squirrel search algorithm is chosen as a meta-heuristic algorithm that utilizes the stated fitness *** results indicate that quality constraint is fulfilled,and the proposed technique gives improved robustness against different attacks with several quality *** proposed technique could be practically implemented in several multimedia applications such as the films industry,medical imagery,OOT platforms,etc.

关键词： Meta-heuristic algorithm constrain optimization problem fitness fiction frame selection squirrel search algorithm

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Sine Cosine Embedded squirrel search algorithm for Global Optimization and Engineering Design

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第4期27卷 4415-4448页

作者： Zeng, Liang Shi, Junyang Li, Ming Wang, Shanshan HuBei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Hubei Peoples R China Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Wuhan 430068 Hubei Peoples R China Hubei Univ Technol Xiangyang Ind Inst Xiangyang 441100 Hubei Peoples R China

The squirrel search algorithm (SSA) is an innovative optimization method that takes inspiration from the foraging and gliding behavior of squirrels. Despite its simple structure and stable performance, it is prone to the same issues as other algorithms, such as falling into local optima and experiencing premature convergence. To address this problem, this paper proposes an improved squirrel search algorithm embedded with the Sine Cosine algorithm (SCSSA). Firstly, the Sine Cosine algorithm is introduced into the SSA to enhance its local exploitation ability. Secondly, the Sobol sequence is utilized to generate the initial population, resulting in higher quality initial solutions. Thirdly, dimensional learning is applied to squirrels on both hickory and oak trees, promoting population diversity and preventing local optima. Finally, the glide constant Gc in SSA is adjusted to decay nonlinearly with iteration count, starting with a large value that gradually decreases in the early stage to facilitate global exploration, and then rapidly decreasing in the later stage to promote local exploitation. Extensive experiments are conducted on 23 classic benchmark functions, the CEC2017 test set, and three engineering problems. The experimental results show that SCSSA can effectively maintain population diversity and can achieve a balance between exploration and exploitation. It consistently outperforms the comparison algorithms in terms of numerical optimization and convergence rate.

关键词： squirrel search algorithm Sine cosine algorithm Dimensional learning Sobol Optimization

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Simulated squirrel search algorithm: A hybrid metaheuristic method and its application to steel space truss optimization

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STEEL AND COMPOSITE STRUCTURES 2022年第4期45卷 579-590页

作者： Pauletto, Mateus P. Kripka, Moacir Univ Passo Fundo Dept Civil Engn Campus 1 BR-99001970 Passo Fundo RS Brazil

One of the biggest problems in structural steel calculation is the design of structures using the lowest possible material weight, making this a slow and costly process. To achieve this objective, several optimization methods have been developed and tested. Nevertheless, a method that performs very efficiently when applied to different problems is not yet available. Based on this assumption, this work proposes a hybrid metaheuristic algorithm for geometric and dimensional optimization of space trusses, called Simulated squirrel search algorithm, which consists of an association of the wellestablished neighborhood shifting algorithm (Simulated Annealing) with a recently developed promising population algorithm (squirrel search algorithm, or SSA). In this study, two models are tried, being respectively, a classical model from the literature (25-bar space truss) and a roof system composed of space trusses. The structures are subjected to resistance and displacement constraints. A penalty function using Fuzzy Logic (FL) is investigated. Comparative analyses are performed between the squirrel search algorithm (SSSA) and other optimization methods present in the literature. The results obtained indicate that the proposed method can be competitive with other heuristics.

关键词： fuzzy logic simulated annealing space trusses squirrel search algorithm structural optimization

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Large-scale economic load dispatch using squirrel search algorithm

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INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT 2020年第6期14卷 1351-1380页

作者： Sakthivel, V. P. Suman, M. Sathya, P. D. Govt Coll Engn Dept Elect & Elect Engn Dharmapuri India Annamalai Univ Fac Engn & Technol Dept Elect Engn Chidambaram India Annamalai Univ Fac Engn & Technol Dept Elect & Commun Engn Chidambaram India

Purpose Economic load dispatch (ELD) is one of the crucial optimization problems in power system planning and operation. The ELD problem with valve point loading (VPL) and multi-fuel options (MFO) is defined as a non-smooth and non-convex optimization problem with equality and inequality constraints, which obliges an efficient heuristic strategy to be addressed. The purpose of this study is to present a new and powerful heuristic optimization technique (HOT) named as squirrel search algorithm (SSA) to solve non-convex ELD problems of large-scale power plants. Design/methodology/approach The suggested SSA approach is aimed to minimize the total fuel cost consumption of power plant considering their generation values as decision variables while satisfying the problem constraints. It confers a solution to the ELD issue by anchoring with foraging behavior of squirrels based on the dynamic jumping and gliding strategies. Furthermore, a heuristic approach and selection rules are used in SSA to handle the constraints appropriately. Findings Empirical results authenticate the superior performance of SSA technique by validating on four different large-scale systems. Comparing SSA with other HOTs, numerical results depict its proficiencies with high-qualitative solution and by its excellent computational efficiency to solve the ELD problems with non-smooth fuel cost function addressing the VPL and MFO. Moreover, the non-parametric tests prove the robustness and efficacy of the suggested SSA and demonstrate that it can be used as a competent optimizer for solving the real-world large-scale non-convex ELD problems. Practical implications This study has compared various HOTs to determine optimal generation scheduling for large-scale ELD problems. Consequently, its comparative analysis will be beneficial to power engineers for accurate generation planning. Originality/value To the best of the authors' knowledge, this manuscript is the first research work of using SSA approach for

关键词： Artificial intelligence Thermal power Cost minimization Energy conversion optimization Economic load dispatch Multi fuels Non-convex fuel cost function squirrel search algorithm Valve point effect Large scale power system

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Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2020年第4期23卷 891-902页

作者： Sanaj, M. S. Prathap, P. M. Joe Satyabhama Univ Dept Comp Sci & Engn Chennai Tamil Nadu India RMD Engn Coll Dept Informat Technol Chennai Tamil Nadu India

Task scheduling in the cloud platform seems to be the most significant issue to guarantee that cloud connectivity adequately and efficiently meets the requirements of customers. Scheduling is basically the method of mapping or assigning tasks after taking into account job features to accessible funds. An effective scheduling protocol should comply with user needs and aids a service provider perform excellent quality of service (QoS) in order to boost general application efficiency. Cloud computing is an evolving computational paradigm with a broad range of self-reliant and economically diverse computational structures. Task scheduling is a significant move to enhance cloud computing general efficiency. Task scheduling is also important in order to decrease power utilization and enhance service providers' profitability through a reduction in handling moment. In this paper we suggest a chaotic squirrel search algorithm (CSSA) to optimally multitask scheduling in an Infrastructure as a Service (IaaS) cloud atmosphere. The methods continuously generate job plans that render the current approaches more cost-effective. In order to guarantee greater global convergence, the early eco system was produced with messy optimisation for the efficient eco-system. The suggested chaotic squirrel search algorithm was ultimately synthesised with the messy local search to enable the exploring authority to complement squirrel search algorithm (SSA) algorithms. Other QoS conditions such as compatibility and safety for very big cases can be expanded to cover the suggested technique. A cloud simulator toolkit takes into consideration the strategy and compares the outcomes with scheduling algorithms so that ideal outcomes for several goals are achieved. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.

关键词： Task scheduling QoS Multitask scheduling IaaS cloud atmosphere squirrel search algorithm Chaotic squirrel search algorithm

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