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IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Saitou, Hayata Haraguch, Harumi Ibaraki Univ Sci & Engn Ibaraki Japan

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

Grey wolf optimization (GWO) algorithm is a swarm intelligence optimization technique that is developed by Mirjalili to mimic the hunting behavior and leadership hierarchy of grey wolves in nature. oppositional-based learning (OBL) is a new concept, which has attracted many research efforts in the last decade, some optimization methods have already used the concept of OBL to improve their performance. This paper presents an efficient algorithm, namely, Improved Mutation oppositional-based learning GWO (IMOGWO) based on Quasi oppositional-based learning (QOBL) and Topological oppositional-based learning (TOBL) with some parameter adjustments to improve exploration and exploitation. The experiments were executed 28 widely used benchmark test functions with various features. The results reveal that the proposed algorithm improves the exploration capability and is effective, ranking first in average value compared to the comparison algorithm.

关键词： Swarm optimization Grey Wolf Optimization oppositional based learning Exploration Exploitation

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Quasi oppositional Dragonfly Algorithm for Load Balancing in Cloud Computing Environment

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期122卷 2639-2656页

作者： Latchoumi, T. P. Parthiban, Latha SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Pondicherry Univ Community Coll Dept Comp Sci Pondicherry India

In cloud computing (CC), load balancing tasks remain a critical problem in distributing resources from a data center. Ensure that every virtual machine (VM) has a balanced load to maximize capacity utilization. In the CC world, load balancing is a Non-Polynomial (NP) problem resolved with metaheuristic algorithms. A new Quasi-oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) has been developed to obtain optimum resource scheduling in a CC configuration. The proposed QODA-LB algorithm uses three variables to calculate an objective function: execution time, execution cost, and charge. The QODA-LB algorithm assigns tasks to VM according to its potential and the resulting objective function. Also, the QODA-LB algorithm employs the Quasi-oppositional based learning principle to increase the standard convergence rate of the Dragonfly (DA) algorithm. A complete series of experiments were conducted, and the results were analyzed in various ways to ensure the increased efficiency of the QODA-LB algorithm. Simulation results demonstrated an optimal load balancing efficiency and outperformed key approaches.

关键词： Cloud computing Load scheduling Dragonfly algorithm oppositional based learning

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A novel ameliorated Harris hawk optimizer for solving complex engineering optimization problems

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第12期36卷 7641-7681页

作者： Mahapatra, Sheila Dey, Bishwajit Raj, Saurav Alliance Univ Dept Elect & Elect Engn Bangalore Karnataka India IIT ISM Dept Elect Engn Dhanbad Bihar India Gandhi Inst Engn & Technol Dept Elect & Elect Engn Gunupur Odisha India Inst Chem Technol Dept Elect Engn Marathwada Campus Jalna India

This article presents a metaheuristic algorithm namely Ameliorated Harris Hawk Optimizer (AHHO) to solve multiconstrained optimization problems. AHHO mimics the hunting behavior of Harris Hawks and is modified to draw a synergy between exploration and exploitation by conducting neighborhood search of already visited iterations utilizing random exploratory forage and local random forage. The oppositional based learning has been incorporated to generate adequate diversity among solutions. The proposed algorithm is substantiated on a set of 23 standard benchmark functions to test its characteristics in finding optimal solutions. The algorithm is mathematically modeled in the current work to obtain solution for voltage constrained reactive power dispatch (VCRPD) problem for IEEE 57 and 118 bus systems. The proposed method of VCRPD is implemented first with shunt capacitors as var sources and then replacing them with static var compensator (SVC) for improved performance. The optimal placement of SVC is determined by fast voltage stability index method. The proposed AHHO algorithm reduces the transmission losses by 9.42% with shunt capacitors in IEEE 57 bus and 10.344% in IEEE 118 bus system. Similarly, transmission loss with SVC compensated system is reduced by 12.86% in IEEE 57 bus and 14.74% in IEEE 118 bus system. Results reveal the proposed technique has the potent to solve real world optimization problems and is competitive with recent methods reported in state-of- art literature.

关键词： active power loss ameliorated Harris Hawk optimizer oppositional based learning voltage constrained reactive power dispatch

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oppositional real coded chemical reaction based optimization to solve short-term hydrothermal scheduling problems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 63卷 145-157页

作者： Bhattacharjee, Kuntal Bhattacharya, Aniruddha Dey, Sunita Halder Nee Dr BC Roy Engn Coll Durgapur 713206 W Bengal India Natl Inst Technol Agartala Agartala 799046 India Jadavpur Univ Dept Elect Engn Kolkata 700032 W Bengal India

This paper presents an oppositional Real Coded Chemical Reaction based (ORCCRO) algorithm to solve the short-term hydrothermal scheduling (STHS) problem. Being complex, the hydrothermal system relates with every problem variables in a non-linear way. The objective of the STHS is to determine the optimal hourly schedule of power generation for different hydrothermal power system for certain intervals of time to minimize the total cost of power generations. Chemical Reaction Optimization (CRO) imitates the interactions of molecules in terms of chemical reaction to reach a lower energy stable state. A real coded version of CRO, known as Real-Coded Chemical Reaction Optimization (RCCRO) is considered here. oppositional based RCCRO (ORCCRO) added here to improve the quality of solutions with minimum time. The proposed opposition-based RCCRO (ORCCRO) employs opposition-based learning i.e., generation of quasi-opposite numbers for population initialization instead of pseudo random numbers to improve the convergence rate of the RCCRO. To check the effectiveness of the ORCCRO, 3 test systems are considered, mathematically remodeled to make it apt for solving short-term hydrothermal scheduling problem. Results prove that the proposed approach is better than all existing optimization techniques in terms quality of solution, computational efficiency and robustness to solve STHS problems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Chemical reaction Hydrothermal scheduling oppositional based learning Optimization Real Coded Chemical Reaction Optimization

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A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年第1期212卷 195-223页

作者： Onay, Funda Kutlu Amasya Univ Dept Comp Engn TR-05100 Amasya Turkiye

The chef-based optimization algorithm (CBOA) is a human-based method inspired by the relationship between culinary students and chef instructors. The original CBOA does not have a process that separately controls population generation and updating. For this reason, in this study, a novel improved CBOA with diffusion process (DP) algorithm with the Gaussian walk (CBOADP) is proposed. In this way, it is aimed to transfer promising members to the next iteration. In the original CBOA algorithm, the selection of the chefs is performed randomly. The oppositional-based learning (OBL) approach aims to find the current best chef by updating the chef from the chef candidates selected from opposite regions. Because the selection of the chef plays an important role in the selection of students. To develop the method with optimal parameters, parameter analysis is performed as the DP's walk and maximum diffusion parameters. The new method's performance is evaluated through the CEC2019 and CEC2022 test suites and traditional and advanced methods. Results for real design problems are also reported. All results are supported by the Wilcoxon sign and Friedman rank statistical tests. As a result, in general, the best convergence has been achieved with CBOADP in about 75% of the benchmark test data, and statistically, it has first rank among other methods on average. In addition, CBOADP reached the best fitness value in five of the six compared engineering problems. Accordingly, it is seen that DP and OBL approaches contribute positively to the performance of CBOA and show a successful convergence trend among other compared methods. (c) 2023 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Chef-based optimization algorithm Diffusion process Gaussian walk oppositional based learning Metaheuristic algorithms

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Solution of Short-Term Hydrothermal Scheduling Problem Using Quasi-Reflected Symbiotic Organisms Search Algorithm Considering Multi-fuel Cost Characteristics of Thermal Generator

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第6期43卷 2931-2960页

作者： Das, Sujoy Bhattacharya, Aniruddha Chakraborty, Ajoy Kumar NIT Agartala Dept Elect Engn Agartala 799046 India

Hydrothermal scheduling is an important issue in the field of power system economics. The aim of the short-term hydrothermal scheduling is to optimize the hourly output of power generation for different hydrothermal units for certain intervals of time to minimize the total cost of generations. In this paper, quasi-reflected symbiotic organisms search is implemented for optimal scheduling of hydrothermal problems. In symbiotic organisms search, the word "symbiosis" defines the relationship between two different species. The relationships are mutualism, commensalism and parasitism, depending on which the algorithm works. Further, the quasi-reflected scheme is incorporated into symbiotic organisms search in order to improve the performance of the algorithm. To investigate the performance of quasi-reflected symbiotic organisms search algorithm, the algorithm is tested on three test systems. Along with this, some statistical tests have also been performed. The results obtained by the quasi-reflected symbiotic organisms search algorithm are compared with other recently proposed methods to establish its robustness.

关键词： oppositional based learning Prohibited discharge zone Quasi-reflected symbiotic organisms search Short-term hydrothermal scheduling Statistical analysis

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A Hybrid Optimization Method OWGWA for EEG/ERP Adaptive Noise Canceller With Controlled Search Space

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2020年第3期11卷 30-48页

作者： Nagal, Rachana Kumar, Pradeep Bansal, Poonam Amity Univ Noida India Maharaja Surajmal Inst Technol Delhi India

In this paper, a system for filtering event-related potentials/electroencephalograph is exhibited by adaptive noise canceller through an optimization algorithm, oppositional hybrid whale-grey wolf optimization algorithm (OWGWA). The OWGWA can choose the control parameters of the grey wolf algorithm utilizing whale parameters. To balance out the randomness of optimization strategies another methodology is implemented called controlled search space. Adaptive filter's noise reduction capability has been tested through adding adaptive white Gaussian noise over contaminated EEG signals at different noise levels. The performance of the proposed OWGWA-CSS algorithm is evaluated by signal to noise ratio in dB, mean value, and the relationship between resultant and input ERP. In this work, ANCs are also implemented by utilizing other optimization techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWGWA-CSS technique provides higher SNR value, significantly lower mean and higher correlation as compared to other techniques.

关键词： Adaptive Noise Canceller Controlled Search Space EEG/ERP Signal Genetic Algorithm Gradient-based Algorithm oppositional based learning Optimization Algorithm Swarm-based Algorithm

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Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2016年第4期19卷 1693-1713页

作者： Guha, Dipayan Roy, Provas Kumar Banerjee, Subrata Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India Jalpaiguri Govt Engn Coll Dept Elect Engn Jalpaiguri W Bengal India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

This article presents a newly developed, novel and efficient optimization technique called quasi-oppositional grey wolf optimization algorithm (QOGWO) for the first time to solve load frequency control problem (LFC) of a power system. Grey wolf optimization (GWO) is a recently developed meta-heuristic optimization technique based on the effect of leadership hierarchy and hunting mechanism of wolves in nature. Two widely employed test systems;viz. two-area hydro-thermal and four-area hydro-thermal power plant, are considered to establish the effectiveness of the proposed QOGWO algorithm. Optimal proportional-integral-derivative controller (PID) is designed for each area separately using proposed algorithm employing integral time absolute error (ITAE) based fitness function. The validity of proposed QOGWO method is tangibly verified by comparing its simulation results with those of GWO and other approaches available in the literature. Time domain simulation results confirm the potentiality and efficacy of the proposed QOGWO method over other intelligent methods like fuzzy logic, artificial neural network ( ANN) and adaptive neuro-fuzzy interface system (ANFIS) controller. Finally, sensitivity analysis is performed to show the robustness of the designed controller under different uncertainty conditions. (C) 2016 Karabuk University. Publishing services by Elsevier B.V.

关键词： Load frequency control Grey wolf optimization oppositional based learning Quasi-oppositional based learning Sensitivity analysis

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Quasi-oppositional Gravitational Search Algorithm Applied to Complex Economic Load Dispatch Problem

Quasi-Oppositional Gravitational Search Algorithm Applied to...

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1st International Conference on Non Conventional Energy (ICONCE)

作者： Bulbul, Sk Md Ali Roy, Provas Kumar Dr B C Roy Engn Coll Dept Elect Engn Durgapur India

ISBN: (纸本)9781479933396

This paper proposes an extension of the gravitational search algorithm (GSA) named quasi-oppositional GSA (QOGSA) to solve nonlinear economic load dispatch (ELD) optimization problems by considering valve point loading effects. QOGSA employs opposition-based population initialization and generation jumping to accelerate convergence speed of conventional GSA. ELD is a significant optimization problem in power system to distribute load demand reasonably and scientifically so that fuel cost can be minimized while satisfying various operational constraints. The proposed algorithm is tested in two standard systems of to-unit and 40-unit. Comparison of numerical results with other well established techniques demonstrates higher performance scheme both in quality of solution and computational efficiency.

关键词： Gravitational search algorithm oppositional based learning Quasi oppositional GSA Valve point loading multiple fuels economic load dispatch

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Automatic Generation Control by SMES-SMES Controllers of Two-Area Hydro-Hydro System

Automatic Generation Control by SMES-SMES Controllers of Two...

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1st International Conference on Non Conventional Energy (ICONCE)

作者： Roy, Adhit Dutta, Susanta Roy, Provas Kumar Dr B C Roy Engn Coll Dept Elect Engn Durgapur India

ISBN: (纸本)9781479933396

In this paper, oppositional biogeography based optimization (OBBO) algorithm for load frequency control with interconnected two-area hydro-hydro power system have been investigated. As a consequence of continually load variation, the frequency of the power system changes over time. To stabilize the system frequency oscillations, the active power can be controlled via superconducting magnetic energy storage device (SMES). The significant improvement of optimal transient performance is observed with the addition of SMES unit fitted in both areas. Analysis reveals that SMES unit fitted in either of the areas is as effective as SMES units fitted in both the areas and improves the dynamic performances to a considerable extent following a load disturbance in either of the areas. Gains of the integral controller in LFC loop and parameters of SMES are optimized with the help of OBBO technique. The performance of the proposed OBBO based controller is better as compared with BBO, CRPSO and RGO controller in the presence of SMES. The simulation results indicate the superiority of the proposed OBBO based controller with SMES in terms of system oscillations, settling time and frequency deviation.

关键词： Load frequency control oppositional based learning Biogeography based optimization Transient analysis response superconducting magnetic energy storage device

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