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Combined economic and emission power dispatch problems through multi-objective squirrel search algorithm

APPLIED SOFT COMPUTING 2021年 100卷 106950-106950页

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

Over the years, due to the increasing deterioration of environmental problems, combined economic and environmental power dispatch has become one of the active research areas in the power system operation and control. This paper proposes a new multi-objective squirrel search algorithm to solve the combined economic and environmental power dispatch problem. The proposed multi-objective squirrel search algorithm integrates squirrel search algorithm along with Pareto-dominance principle to generate non-dominated solutions. It uses an external elitist depository mechanism with crowding distance sorting to maintain the distribution diversity of Pareto-optimal solutions as the evolution proceeds. In addition, a fuzzy decision-making strategy is used to select the best compromised solution from the obtained Pareto frontiers. The combined economic and environmental power dispatch problem is also solved by squirrel search algorithm based weighted sum approach, which transfers both the fuel cost and emission objectives into a single-objective function. The performances of multi objective squirrel search algorithm are tested on three test systems with diverse complexities. The simulation results and comparisons with the other state-of-the-art heuristic algorithms confirm that the proposed multi-objective squirrel search algorithm for combined economic and environmental power dispatching can obtain a better trade-off between fuel cost and emission objectives . (c) 2020 Elsevier B.V. All rights reserved.

关键词： Economic emission dispatch Multi-objective optimization Pareto front solution squirrel search algorithm Valve point loading

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A modified squirrel search algorithm based on improved best fit heuristic and operator strategy for bin packing problem

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APPLIED SOFT COMPUTING 2019年 82卷 105565-000页

作者： El-Ashmawi, Walaa H. Abd Elminaam, Diaa Salama Suez Canal Univ Fac Comp & Informat Comp Sci Dept Ismalia City Egypt Benha Univ Fac Comp & Informat Informat Syst Dept Banha Egypt

Bin Packing Problem (BPP) is one of the main optimization problems in which a set of items of known sizes should be packed into a minimum number of bins. In which the total size of items in each bin is not exceeding the bin capacity. Several approximation algorithms have been suggested that provide an approximate solution to the BPP. Nevertheless, they are not applicable to solve large-scale instances within a reasonable time or obtain an optimal number of bins. In this paper, we propose a modified version of the most recent optimization algorithm squirrel search algorithm (SSA) for solving the one-dimensional bin packing problem (MSBPP). The proposed algorithm is based on an improved best fit heuristic for generating a feasible initial packing of items into bins. During improving the solution, the operator's strategy can take place to obtain an optimized solution for the BPP. To the best of our knowledge, this paper is the first work of applying squirrel search algorithm in a bin packing problem as an example of the optimization problem. The modified algorithm has been tested on various benchmark datasets ranged from easy to hard class dataset. As well as, we compare it against other popular algorithms such as Particle swarm optimization (PSO), African buffalo optimization (ABO) and Crow search algorithm (CSA) to demonstrate its efficiency and accuracy. According to the results of an easy class, the proposed algorithm has achieved an improvement ranged from 16% to 32% for N2C1W1_A instance when compared with other algorithms to reach an optimal result with the least number of iterations. In addition, the average execution time of it beats other compared algorithms with improvement reached up to 91% faster than PSO, 98% faster than ABO and 94% faster than CSA. For medium class, the bin efficiency of the proposed algorithm has been improved by 26% during iterations for N1W1B2R9 instance with minimum number of iterations and least execution time while for hard class,

关键词： Bin packing problem Optimization algorithms squirrel search algorithm Best fit heuristic Operator strategy

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A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions

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ENERGY CONVERSION AND MANAGEMENT 2021年 230卷 113773-113773页

作者： Fares, Dalila Fathi, Mohamed Shams, Immad Mekhilef, Saad Univ MHamed Bougara Lab Signaux & Syst Inst Genie Elect & Elect Ave Independence Boumerdes 35000 Algeria UDES CDER Unite Dev Equipements Solaires Ctr Dev Energies Renouvelables Tipasa 42415 Algeria Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab PEARL Fac Engn Kuala Lumpur 50603 Malaysia Swinburne Sch Software & Elect Engn Melbourne Vic Australia

The partial shading condition (PSC) makes it challenging for the PV system to harvest maximum power via maximum power point tracking (MPPT). Various MPPT algorithms based on bio-inspired optimization methods were proposed in the literature. The mechanism employed by these algorithms varies from one to another, making them perform differently when tracking the GMPP. This paper introduces a novel MPPT technique based on the improved squirrel search algorithm (ISSA). The performance of the proposed ISSA improved the tracking time by 50% in comparison with the conventional SSA algorithm. Similarly, the proposed method has also been compared with popular Genetic algorithm (GA), and particle swarm optimization (PSO). The results proved the ability of the proposed algorithm in tracking the GMPP with faster convergence and fewer power oscillations in comparison. The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.

关键词： Global maximum power point Partial shading PV system Bio-inspired algorithm squirrel search algorithm

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Constrained fault-tolerant thrust allocation of ship DP systembased on a novel quantum-behaved squirrel search algorithm

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OCEAN ENGINEERING 2022年第Part4期266卷

作者： Yu, Zhihao Du, Jialu Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Liaoning Peoples R China

The thrust allocation (TA) of ship dynamic positioning (DP) systems is to determine the desired thrust force and direction of each thruster of a ship such that all the thrusters jointly produce the generalized control forces commanded by DP controllers. Thruster faults inevitably occur because thrusters operate in the complex ocean environment for a long time. In addition, due to physical limitations of thrusters, the desired thrust force and direction of thrusters both have magnitude and rate constraints. In the presence of loss-of-effectiveness (LOE) faults as well as magnitude and rate constraints of thrusters of ships, the TA of their DP systems can be formulated as a constrained fault-tolerant optimization problem. This paper creates a novel quantum-behaved squirrel search algorithm (QSSA) by combining the squirrel search algorithm with a quantum Delta potential well model, and whereby solves this constrained fault-tolerant TA optimization problem. Simulation studies on a scale model supply ship verify the effectiveness of the QSSA-based constrained fault-tolerant TA of the DP ship under thruster LOE faults.

关键词： Dynamic positioning Thrust allocation Fault-tolerant Optimization squirrel search algorithm

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A squirrel search algorithm for the multi-objective portfolio optimisation with transaction costs

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SCIENTIFIC AFRICAN 2024年 24卷

作者： Salb, M. Nassir-Ud-Diin Ebrahim Gopaul, Ashvin Cheeneebash, Jayrani Univ Mauritius Dept Math Reduit Mauritius

Portfolio optimisation (PO) is the problem of deciding how much of an investor's wealth should be invested in each asset amongst a universe of assets so as to obtain a reasonable trade -off between return and risk objectives. In this paper, we address this problem including reallife constraints such as cardinality, quantity and pre-assignment constraints by making use of an improved version of the squirrel search algorithm (SSA) described in the literature. The novelty of this work is the application of the improved SSA in the multi-objective portfolio optimisation problem together with transaction costs. There are four major improvements from the classical SSA with the aim of better global convergence ability. The first improvement is to consider the predator presence probability as a variable, the second is to generate the squirrels' position by using a cloud generator. The third and fourth improvements consist of selecting the best squirrel following successive iteration and to find the optimal dimensions of the position of the best flying squirrel. We test the performance of the algorithm with seven publicly available datasets drawn from two different sources and compare the results with wellknown heuristics and the standard SSA. The results obtained proved the efficacy and superiority of the improved algorithm considering both the cases with and without transaction costs. The improved SSA (ISSA) obtained smaller generational distance and inverted generational distance when compared to SSA for most of the test problems except for the S&P 100 index. In terms of the hypervolume indicator it can be seen that the ISSA outperformed all the algorithms as it obtained higher values for all the test problems. For the spacing metric ISSA performed better for most datasets.

关键词： Portfolio optimisation Multi-objective squirrel search algorithm Normal cloud generator Transaction cost

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Energy Efficient Cluster Head Selection using Hybrid squirrel Harmony search algorithm in WSN

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第12期10卷 477-487页

作者： Lavanya, N. Thangavelu, S. Vellore Inst Technol Sch Elect Engn Dept Commun Engn Vellore Tamil Nadu India

The Wireless Sensor Network (WSN) has found an extensive variety of applications, which include battlefield surveillance, monitoring of environment and traffic, modern agriculture due to their effectiveness in communication. Clustering is one of the significant mechanisms for enhancing the lifespan of the network in WSN. This clustering scheme is exploited to improve the sensor network's lifespan by decreasing the network's energy consumption and increasing the stability of the network. The existing cluster head selection algorithm suffers from the inconsistent tradeoffs between exploration exploitation and global search constraints. Therefore, in this research, the hybridization of two popular optimization algorithms, namely, Harmony search algorithm (HSA) and squirrel search algorithm (SSA) is executed for optimal selection of cluster heads in WSN with respect to distance and energy. The proposed Hybrid squirrel Harmony search algorithm (HSHSA) is found to be energy efficient when compared with first node death (FND) and last node death (LND) of existing Cluster Head Selection (CHS) techniques. In addition to this, the proposed HSHSA shows enhancements in overall throughput and residual energy of the wireless sensor network by 31.02% and 85.69%, respectively than the existing algorithms.

关键词： Cluster head selection clustering harmony search algorithm squirrel search algorithm wireless sensor network

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Ischemic Heart Disease Prediction Using Optimized squirrel search Feature Selection algorithm

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IEEE ACCESS 2022年 10卷 122995-123006页

作者： Cenitta, D. Arjunan, R. Vijaya Prema, K. V. Manipal Acad Higher Educ MAHE Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 Karnataka India Manipal Acad Higher Educ MAHE Manipal Inst Technol Dept Comp Sci & Engn Bengaluru 560064 Karnataka India

In recent years, the volume in globally recognized medical data sets are increasing both with attributes and number of records. Machine learning algorithms aiming to detect and diagnose ischemic heart diseases requires high efficacy and judgment. The state of art Ischemic heart disease data sets presents several issues, including feature selection, sample size, sample imbalance, and lack of magnitude for some characteristics etc. The proposed study is primarily concerned with improving feature selection and reducing the number of features yet giving better decisions. In this study, to pick salient aspects of heart illness, an improved squirrel search optimization algorithm with a meta-heuristic approach is proposed. Comparison study of the proposed ischemic heart disease squirrel search optimization (IHDSSO) model in conjunction with random forest classifier ensures better feature selection and accuracy over 98% with respect to other state-of-the-art optimization algorithms.

关键词： Machine learning feature selection squirrel search algorithm random forest algorithm University of California Irvin dataset

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A discrete squirrel search optimization based algorithm for Bi-objective TSP

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WIRELESS NETWORKS 2021年 1-15页

作者： Liu, Zhuoran Zhang, Fanhao Wang, Xinyuan Zhao, Qidong Zhang, Changsheng Liu, Tianhua Zhang, Bin Northeastern Univ Shenyang 110819 Peoples R China Shenyang Normal Univ Shenyang 110034 Peoples R China

This paper presents a novel discrete squirrel search optimization algorithm for the bi-objective traveling salesman problem (TSP). Firstly, the squirrel search algorithm, a single-objective optimization algorithm, needs to be improved to a multi-objective optimization algorithm. This paper designs a mapping fitness function according to the Pareto sorting level and grid density to evaluate all the feasible solutions and applies a selection probability based on the roulette wheel selection. Then, this paper implements this algorithm and other algorithms on classic multi-objective test functions to analyze solutions' convergence and diversity. It is concluded that it has a good performance in solving multi-objective problems. Moreover, based on this multi-objective squirrel search algorithm, this paper then designs an encoding method to initialize solutions, applies a crossover operator to the squirrel migration process, and utilizes a mutation operator to the squirrel mutation stage. In this case, a discrete squirrel search optimization for the bi-objective traveling salesman problem (TSP) is finally designed. And this paper analyzes the results of this algorithm and other algorithms running on classic bi-objective TSPs. As a result, the presented algorithm's solutions are also superior to other algorithms for convergence and spread.

关键词： squirrel search algorithm Bi-objective traveling salesman problem Non-dominated sorting Mapping fitness evaluation Crosser operation

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BIRD squirrel OPTIMIZATION WITH DEEP RECURRENT NEURAL NETWORK FOR PROSTATE CANCER DETECTION

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INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING 2025年第1期23卷 47-67页

作者： Kumar, Goddumarri Vijay Ismail, B. Mohammed Reddy, T. Bhaskara Tahernezhadi, Mansour Alam, Mansoor Sri Krishnadevaraya Univ Dept Comp Sci & Technol Anantapur 515003 Andhra Prades India PA Coll Engn Dept Artificial Intelligence & Machine Learning Mangalore India Visvesvaraya Technol Univ Belgavi India Sri Krishnadevaraya Univ Dept Comp Sci & Technol Anantapuramu A.P India Northern Illinois Univ Dept Elect Engn Coll Engn & Engn Technol 590 Garden Rd De Kalb IL 60115 USA Northern Illinois Univ Dept Elect Engn Coll Engn & Engn Technol 590 Garden Rd De Kalb IL 60115 USA

Prostate cancer is solid organ melanoma which increases mortality amongst humans. Automatic techniques for determining prostate cancer from magnetic resonance images (MRI) are highly recommended. Conventional techniques adapt different steps, which may result in huge computational costs. In order to perform automated prostate cancer classification with MRI, a deep model named bird squirrel (BS) algorithm-based deep recurrent neural networks (DeepRNN) is developed in this research. Here, the MRI noise is removed using a non-local means (NLM) filter. Convolution neural networks (CNN) are also widely used to create segments in order to extract notable features, and they are used in DeepRNN for detecting prostate cancer. To train the classifier, the proposed BS algorithm is used. By combining the bird search algorithm (BSA) and squirrel search algorithm (SSA), the created BS is produced. The evaluation is done using Prostate MRI Dataset and the invented BS-DeepRNN is obtained with a higher accuracy of 0.937, a sensitivity of 0.958, and a specificity of 0.916.

关键词： magnetic resonance image non-local means filter convolution neural network bird search algorithm squirrel search algorithm deep recurrent neural network

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Reconfigured single- and double-diode models for improved modelling of solar cells/modules

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Celik, Emre Karayel, Mehmet Maden, Dincer Abdel-Salam, Mahmoud Ozturk, Nihat Kaplan, Orhan Tejani, Ghanshyam G. Sharma, Sunil Kumar Baljon, Mohammed Duzce Univ Engn Fac Dept Elect & Elect Engn Duzce Turkiye Cankiri Karatekin Univ Vocat Sch Dept Elect & Energy Cankiri Turkiye Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt Gazi Univ Technol Fac Dept Elect & Elect Engn Ankara Turkiye Yuan Ze Univ Dept Ind Engn & Management Taoyuan 320315 Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Majmaah 11952 Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Comp Engn Majmaah 11952 Saudi Arabia

Proper modeling of PV cells/modules through parameter identification based on the real current-voltage (I-V) data is important for the efficiency of PV systems. Most related works have concentrated on the classical single-diode model (SDM) and double-diode model (DDM) and their parameter extraction by various metaheuristic algorithms. In order to render more accurate and representative modeling, this paper adds a small resistance in series with the diodes in SDM and DDM. The new models are named reconfigured SDM (Reconfig-SDM) and reconfigured DDM (Reconfig-DDM), and they have not been studied so far as we know. A squirrel search algorithm (SSA) is employed to globally find the parameters of the new models. The performance achieved is experimentally tested on both a commercial RTC France solar cell and a CS6P-220P polycrystalline PV module located at D & uuml;zce University in T & uuml;rkiye. A vivid comparison of experimental findings, observation, and analysis clearly demonstrates that the proposed Reconfig-SDM and Reconfig-DDM tuned by the SSA have better capacity and effectiveness for modeling PV devices than some cutting-edge approaches. Specifically, compared with the best-performing approach in the literature, Reconfig-SDM and Reconfig-DDM could reduce the error rate up to 0.37% and 2.58% for the solar cell, and 3.21% and 29.0% for the solar module.

关键词： PV modeling I-V characteristic Reconfigured SDM Reconfigured DDM Parameter identification squirrel search algorithm

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