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Crisscross differential evolution algorithm for constrained hydrothermal scheduling

APPLIED SOFT COMPUTING 2020年 93卷 106393-106393页

作者： Kaur, Manbir Dhillon, J. S. Kothari, D. P. Thapar Inst Engn & Technol Elect & Instrumentat Engn Dept Patiala 147004 Punjab India Sant Longowal Inst Engn & Technol Elect & Instrumentat Engn Dept Sangrur 148106 Punjab India Wainganga Coll Engn & Management Nagpur 441114 Maharashtra India

This paper proposes a novel chaotic-crisscross differential evolution (CCDE) algorithm to realize an optimal generation schedule of multi-chain short-term hydrothermal system over 24 hours' time-horizon in a multi-objective framework, considering conflicting economic-environmental aspects of thermal units. The equality constraints of active power balance and the amount of available water are independently handled using variable elimination method. However, the statistical uncertainties called residues arise due to infringements of equality constraints while adjusting the violated dependent variables within their boundaries. These residues are fuzzy quantified within their prescribed bounds, and are embedded as objectives to be optimized. An interactive unified fuzzy satisfying function is aimed to solve the conflict of three objectives. The global solution accuracy and convergence rate of stochastic algorithms are significantly affected by parameter-tuning, exploration and exploitation strategies. The proposed algorithm integrates dual crisscross mechanism orthogonally with chaotically tuned DE to balance exploration and exploitation. Information collected about non-dominated solutions from search space is processed using opposition-based learning for better accuracy of global solution in three-dimensional objective function hyperspace. The numerical results show improvement in unified satisfying objective function and convergence performance metrics over the existing methods. The competence of the proposed algorithm is confirmed through illustrations on benchmark functions and is substantiated through statistical significance tests. (C) 2020 Published by Elsevier B.V.

关键词： Crisscross differential evolution Fuzzy set theory Hydrothermal scheduling multi-objective optimization problem Opposition-based learning

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An adaptive population multi-objective quantum-inspired evolutionary algorithm for multi-objective 0/1 knapsack problems

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INFORMATION SCIENCES 2013年 243卷 39-56页

作者： Lu, Tzyy-Chyang Yu, Gwo-Ruey Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Chiayi Taiwan Natl Chung Cheng Univ Dept Elect Engn Adv Inst Mfg High Tech Innovat Chiayi Taiwan

The multi-objective quantum-inspired evolutionary algorithm (MQEA) is a relatively recent technique for solving multi-objective optimization problems (MOPs). In the MQEA, quantum bit (Q-bit) individuals are classified into several groups, with each group assigned one objective solution (one of the non-dominated solutions found so far) as the reference sign string. For a fixed population size, the number of Q-bit individuals assigned to each objective solution decreases with increasing number of found non-dominated solutions. As a result, more or fewer Q-bit individuals assigned to each objective solution may lead a confused order of local and global search. To mitigate this issue, an adaptive population MQEA (APMQEA) is proposed in this work. In the APMQEA, the number of Q-bit individuals assigned to each objective solution is fixed, and the population size is adaptively changed according to the number of found non-dominated solutions. Experimental results for the multi-objective 0/1 knapsack problem show that the APMQEA finds solutions close to the Pareto-optimal front and maintains a good spread of the non-dominated set. (C) 2013 Elsevier Inc. All rights reserved.

关键词： multi-objective quantum-inspired evolutionary algorithm multi-objective optimization problem Quantum bits multi-objective 0/1 knapsack problem

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Goal Programming Approach for Energy Management of Smart Building

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IEEE ACCESS 2022年 10卷 25341-25348页

作者： Foroozandeh, Zahra Ramos, Sergio Soares, Joao Vale, Zita Polytech Inst Porto Sch Engn ISEP Res Grp Intelligent Engn & Comp Adv Innovat & Dev P-4200072 Porto Portugal

In this paper, a collective residential building is considered in which the following points are taken into consideration: (i) a flexibility value of Contract Power (CP) is considered for each consumer;(ii) it is assumed a single CP for the entire building;(iii) an energy resource manager entity is considered to manage the energy resources in the residential building, such as Electric Vehicles (EVs), Photovoltaic (PV) generation system, and the Battery Energy Storage System (BESS). Taking into consideration the previous assumptions, the major goal of this work is to minimize the electricity consumption costs of the residential building by using a multi-objective Mixed-Binary Linear Programming (MOMBLP) formulation. The objective function of the MOMBLP model minimizes the electricity cost consumption of each apartment. Then, a Goal Programming (GP) strategy is applied to find the most appropriate solutions for the proposed MOMBLP model. Finally, the performance of the suggested model is evaluated by comparing the obtained results from a Single-objective Mixed-Binary Linear Programming (SOMBLP) approach in which the whole building consumption cost is minimized. The results show that using the GP strategy a reduction of 7.5% in the total annual energy consumption is verified in comparison with SOMBLP. Moreover, the GP approach leads to fair benefit among building consumers, by finding a solution with less distance from the desired level.

关键词： Costs Buildings Linear programming Energy management Indexes Energy management systems Batteries Energy management goal-programming methodology multi-objective optimization problem renewable energy smart building

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Hybrid multi-objective Genetic Algorithm for multi-objective optimization problems

Hybrid Multi-Objective Genetic Algorithm for Multi-Objective...

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第27届中国控制与决策会议

作者： Song Zhang Hongfeng Wang Di Yang Min Huang College of Information Science and Engineering Northeastern UniversityState Key Laboratory of Synthetical Automation for Process IndustriesNortheastern University

ISBN: (纸本)9781479970186

As a result of important practical significance in real-world engineering applications,multi-objective optimization problem has been one of scientific problems concerned by many *** recent years,genetic algorithm(GA) has begun to be widely used to solve a variety of multi-objective optimization problems due to its population-based search *** this paper,NSGA-II,which is a most classical multi-objective GA,is investigated and discussed in *** order to address the problem of exploitation lacking in the search process of NSGA-II,a local search strategy,which is able to applied in multi-objective optimization domain,is proposed and led into NSGA-II *** on a set of benchmark test functions,the experimental results show that the proposed algorithm has demonstrated superior to NSGA-II in terms of convergence and distribution.

关键词： multi-objective optimization problem Genetic algorithm Hybrid genetic algorithm Local search

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Bond-slip model for coarse sand coated interface between FRP and concrete from optimization technique

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COMPUTERS & STRUCTURES 2006年第7期84卷 439-449页

作者： Cho, KH Cho, JR Chin, WJ Kim, BS Korea Inst Construct Technol Struct Res Dept Goyang 411712 South Korea

Innovative FRP-concrete composite bridge deck systems fabricated by casting concrete above FRP panel have been proposed and have already been implemented actually. In such deck systems, the FRP panel plays the role of formwork during the curing of concrete and behaves as a structural member combined with concrete during service life. Coarse sand coating is usually applied to secure composite behavior of the two materials instead of epoxy bonding, which is extensively used for retrofit purpose of deteriorated structures. This study focuses on investigating the bond behavior of coarse sand coating experimentally and analytically. Pure shear tests on coarse sand coated and epoxy coated joints are performed from which results are used to extract the bond-slip model. A method to obtain the bond-slip model from pure shear test is proposed, in which analytic solution of bond-slip model based on fracture mechanics is introduced and physical programming technique is used for formulation. Genetic algorithm is adopted for the optimization. Application of the proposed method produces local bond-slip model for coarse sand coating. Finally, in order to verify the proposed method and local bond-slip model, 4-point bending test of beam specimens with coarse sand coated FRP plate is performed and the result is compared to finite element analysis including the proposed local bond-slip model. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： FRP plate-concrete composite bridge deck FRP-concrete joint coarse sand coating bond-slip model physical programming genetic algorithm multi-objective optimization problem

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optimization of the synchronous motor with hybrid permanent magnet excitation system

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2015年第2期34卷 448-455页

作者： Knypinski, Lukasz Nowak, Lech Demenko, Andrzej Poznan Univ Tech Inst Elect Engn & Elect Poznan Poland

Purpose - The purpose of this paper is to elaborate an algorithm and the software for the rotor structure optimization of the permanent magnet synchronous motor (PMSM) with a magnet composed of two materials made with the use of different technologies: sintered Neodymium magnets and powder dielectromagnets. To execute of optimization of selected motor structure using the non-deterministic procedure. Design/methodology/approach - The mathematical model of the devices includes: the equation of the electromagnetic field, the electric circuit equations and equation of mechanical motion. The numerical implementation is based on finite element method and step-by-step algorithm. The genetic algorithm has been applied in the optimization procedures. The computer code has been developed. Findings - The elaborated computer software has been applied for the optimization and design of PMSMs. The elaborated algorithm has been tested and a good convergence has been attained. The parameters of two optimal structures of PMSM motors have been compared. Originality/value - The presented approach and computer software can be successfully applied to the design and optimization of different structure of PMSM with different type of rotors.

关键词： Electric machines Finite element analysis Genetic algorithm multi-objective optimization problem Permanent magnet synchronous motors

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A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor

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JOURNAL OF SUPERCOMPUTING 2017年第3期73卷 1235-1256页

作者： Rizk-Allah, Rizk M. El-Sehiemy, Ragab A. Deb, Suash Wang, Gai-Ge Univ Minoufia Dept Basic Engn Sci Shibin Al Kawm Egypt Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafr Al Sheikh Egypt Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun 130117 Peoples R China

This paper addresses a novel multi-objective fruit fly optimization algorithm (MOFOA) for solving multi-objective optimization problems. The essence of MOFOA lies in its having two characteristic features. For the first feature, a population of random fruit flies initializes the algorithm. During this initialization phase, the dominated fruit fly is replaced by the nearest non-dominated one. Subsequently, the fruit flies undergo evolution by flying randomly around the non-dominated solution or around the reference point, i.e., the best location of the individual objectives. Afterwards, the fruit flies are updated according to the nearest location whether from the reference point or the previous non-dominated location. For the second feature, the weighted sum method is incorporated to update the previous best locations of fruit flies and the reference point to emphasize the convergence of the non-dominated solutions. To prove the capability of the proposed MOFOA, two standard benchmark problems in addition to the real world application, namely, multi-objective shape design of tubular linear synchronous motor (TLSM) are checked. The corresponding TLSM objective functions aims to maximize operating force and to minimize the flux saturation. The outcomes clearly demonstrate the effectiveness of the proposed algorithm for finding the non-dominated solutions.

关键词： Fruit fly optimization algorithm multi-objective optimization problem Pareto-optimal solutions Reference point Tubular linear synchronous motor

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Nozzle dimension design for aircraft engine infrared signature and thrust active control using MOEA/D

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2020年第15期234卷 2133-2138页

作者： Zhao, Yu Zheng, Shijie AECC Guiyang Engine Design Res Inst Guiyang 550081 Peoples R China Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing Peoples R China

Aircraft infrared signature is one of the most important properties for the military aircraft survivability. In terms of military aircraft, the exhaust system is the most significant infrared radiation source. The exhaust system accounts for more than 90% of the aircraft infrared radiation, and that the exhaust nozzle contributes the most significant infrared radiation of the whole radiation energy provided by the exhaust system from the rear aspect. Low detectionable feature for military aircraft has attracted more importance to promote aircraft survivability via reducing infrared signature. The alteration of nozzle exit area affects an aircraft engine performance;meanwhile, it severely influences the engine infrared signature radiation from the rear side. The present paper is mainly focused on searching an appropriate group of nozzle exit diameter and throat to exit diameter ratio, which can reduce infrared signature radiation while cutting down the loss of thrust. Hence, objectives involve two aspects: one is minimum infrared signature level, and the other is minimum thrust loss. The multi-objective evolutionary algorithm based on decomposition has been employed to solve this bi-objective optimization problem. The optimization results illustrate that dimension selection range and throat to exit diameter ratio exert more important effect on the thrust loss and infrared signature level. Furthermore, the thrust plays significant role for deciding nozzle exit diameter and throat diameter.

关键词： Aircraft engine infrared signature thrust nozzle dimension throat to exit diameter ratio multi-objective optimization problem

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Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios

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SOFT COMPUTING 2024年第6期28卷 5173-5196页

作者： Tabalvandani, Mohammad Ali Nezafat Shirvani, Mirsaeid Hosseini Motameni, Homayun Islamic Azad Univ Sari Branch Dept Comp Engn Sari Iran

The multi-cloud environment (MCE) serves users on-demand by presenting miscellaneous online web services. Each web service which is delivered by every cloud provider has its own quality of features and also own pricing scheme. In the web service composition technology, the integration of the services required by the users is done with the aim of producing the efficient solutions with the desired quality. In some businesses, continuity of activities is very important and a business that fails a lot cannot be trusted by subscribers. In these businesses, it is necessary to maximize the reliability of the system along with minimizing the overall monetary costs. To this end, two new reliability and cost models are presented. All of the network equipment, communication, and elements affecting the total cost and reliability of the system are taken into consideration in the proposed models. Then, the web service composition issue is formulated to a multi-objective optimization problem. To solve this combinatorial problem in large search space of MCE, the multi-objective particle swarm optimization algorithm is suggested to maximize reliability while minimizing the cost of services and make Pareto optimal points. The results of the evaluations show that in different scenarios, the proposed solution proves the amount of 48%, 46%, and 12% averagely improvement over other comparative MOGWO, NSGA-II, and MOEA/D approaches in terms of service failure rate, service implementation cost in cloud providers, and the execution time respectively.

关键词： Web service composition multi-cloud environment Reliability model Cloud cost model multi-objective optimization problem multi-objective particle swarm optimization algorithm

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multi-objective particle swarm optimization with preference information and its application in electric arc furnace steelmaking process

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STRUCTURAL AND multiDISCIPLINARY optimization 2015年第5期52卷 1013-1022页

作者： Feng, Lin Mao, Zhizhong Yuan, Ping Zhang, Bi Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

In this paper, multi-objective particle swarm optimization with preference information (MOPSO-PI) has been proposed. In the proposed algorithm, the information entropy is employed for measuring the probability distribution of particles;the user's preference information is represented as the ranking of each particle through the possible matrix. The optimal procedure is guided by the preference information since the global best performance of particle is randomly chosen among non-dominated solutions with higher ranking value in each iteration. Finally, the MOPSO-PI is applied to optimize the steelmaking process;the power supply curve obtained reduces the electric energy consumption, shortens the smelting time and prolongs the lifespan of the furnace lining. The application results show the effectiveness of the proposed algorithm.

关键词： multi-objective optimization problem Particle swar moptimization (PSO) Preference information Information entropy Power supply curve

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