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IT-CEMOP: An iterative co-evolutionary algorithm for multiobjective optimization problem with nonlinear constraints

APPLIED MATHEMATICS AND COMPUTATION 2006年第1期183卷 373-389页

作者： Osman, M. S. Abo-Sinna, Mahmoud A. Mousa, A. A. Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt High Inst Technol Ramadan Egypt

Over the past few years, researchers have developed a number of multiobjective evolutionary algorithms (MOEAs). Although most studies concentrate on solving unconstrained optimization problems, there exit a few studies where MOEAs have been extended to solve constrained optimization problems. Most of them were based on penalty functions for handling nonlinear constraints by genetic algorithms. However the performance of these methods is highly problem-dependent, many methods require additional tuning of several parameters. In this paper, we present a new optimization algorithm, which is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. The algorithm maintains a finite-sized archive of nondominated solutions which gets iteratively updated in the presence of new solutions based on the concept of epsilon-dominance. The use of epsilon-dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto set approximation by choosing an appropriate e value, which guarantees convergence and diversity. The results, provided by the proposed algorithm for six benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems. (c) 2006 Elsevier Inc. All rights reserved.

关键词： multiobjective nonlinear programming multiobjective evolutionary algorithms genetic algorithms epsilon-dominance

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multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power

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ENERGY 2018年 150卷 1039-1057页

作者： Biswas, Partha P. Suganthan, P. N. Qu, B. Y. Amaratunga, Gehan A. J. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou Henan Peoples R China Univ Cambridge Dept Engn Cambridge England

Economic-environmental power dispatch is one of the most popular bi-objective non-linear optimization problems in power system. Classical economic power dispatch problem is formulated with only thermal generators often ignoring security constraints of the network. But importance of reduction in emission is paramount from environmental sustainability perspective and hence penetration of more and more renewable sources into the electrical grid is encouraged. However, most common forms of renewable sources are intermittent and uncertain. This paper proposes multiobjective economic emission power dispatch problem formulation and solution incorporating stochastic wind, solar and small hydro (run-of-river) power. Weibull, lognormal and Gumbel probability density functions are used to calculate available wind, solar and small-hydro power respectively. Some conventional generators of the standard IEEE 30-bus system are replaced with renewable power sources for study purpose. Network security constraints such as transmission line capacities and bus voltage limits are also taken into consideration alongwith constraints on generator capabilities and prohibited operating zones for the thermal units. Decomposition based multiobjective evolutionary algorithm and summation based multiobjective differential evolution algorithm are applied to the problem under study. An advanced constraint handling technique, superiority of feasible solutions, is integrated with both the multi objective algorithms to comply with system constraints. The simulation results of both the algorithms are summarized, analyzed and compared in this study. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Economic-environmental dispatch Wind power generator Solar photovoltaic Small-hydro power unit Uncertainty modelling multiobjective evolutionary algorithms

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Pareto Front-Based multiobjective Optimization of Distributed Generation Considering the Effect of Voltage-Dependent Nonlinear Load Models

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IEEE ACCESS 2023年 11卷 12195-12217页

作者： Ali, Aamir Abbas, Ghulam Keerio, Muhammad Usman Mirsaeidi, Sohrab Alshahr, Shahr Alshahir, Ahmed Quaid Eawam Univ Engn Sci & Technol QUEST Nawabsha Dept Elect Engn Nawabshah 67450 Sindh Pakistan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Saudi Arabia

Single objective constant PQ load models were extensively considered for site and size of distributed generation (DG) and shunt capacitor (SC) allocation. Which may lead to single non-dominated solution of unpredictable and misleading results about their site and size, loss reduction and payback period. Therefore, primary objective of this study is to investigate the effects of seven nonlinear voltage-dependent load models for the siting and sizing of DG and SC considering various conflicting multiobjective functions. These objective functions are minimization of active power loss, voltage deviation, cost of energy loss per year, the total cost of installed DG and SC, and emission. Three study cases of simultaneous optimization of two and three objective functions are intended to find optimal integration of DG and SC in the standard 33 and 118-bus radial distribution network considering seven nonlinear voltage-dependent load models. A new multiobjective evolutionary algorithm (MOEA) called Bidirectional Coevolutionary (BiCo) is applied to the proposed study cases to demonstrate the impact of load model on DG and SC allocation. Further, to show the superiority and performance of proposed algorithm, six state-of-the-art MOEAs are implemented and statistically compared with proposed algorithm using a representative hypervolume indicator (HVI). The maximum savings in annual cost of annual energy loss reaches 58.99% in case1 of PQ load model, 59.4% in case 2 and 64.96% in case 3 of industrial load model considering only DG allocation, whereas, 93.673% in constant PQ load model of case1, 78.908% in constant current load model of case2 and 93.403% of case3 of PQ load model considering simultaneous DG and SC. Simulation results show that the proposed algorithm is adept and suitable to find a better trade-off between various conflicting objective functions compared to other recently designed MOEAs.

关键词： Load modeling Optimization Linear programming Resource management Reactive power Biological system modeling Voltage control Distributed generation shunt capacitor multiobjective evolutionary algorithms non-linear load models

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Modeling the speed-based vessel schedule recovery problem using evolutionary multiobjective optimization

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INFORMATION SCIENCES 2018年 448卷 53-74页

作者： Cheraghchi, Fatemeh Abualhaol, Ibrahim Falcon, Rafael Abielmona, Rami Raahemi, Bijan Petriu, Emil Larus Technol 170 Laurier Ave W Ottawa ON Canada Univ Ottawa 55 Laurier Ave E Ottawa ON Canada

Liner shipping is vulnerable to many disruptive factors such as port congestion or harsh weather, which could result to delay in arriving at the ports. It could result in both financial and reputation losses. The vessel schedule recovery problem (VSRP) is concerned with different possible actions to reduce the effect of disruption. In this work, we are concerned with speeding up strategy in VSRP, which is called the speed-based vessel schedule recovery problem (S-VSRP). We model S-VSRP as a multiobjective optimization (MOO) problem and resort to several multiobjective evolutionary algorithms (MOEAs) to approximate the optimal Pareto set, which provides vessel route-based speed profiles. It gives the stake-holders the ability to tradeoff between two conflictive objectives: total delay and financial loss. We evaluate the problem in three scenarios (i.e., scalability analysis, vessel steaming policies, and voyage distance analysis) and statistically validate their performance significance. According to experiments, the problem complexity varies in different scenarios, and NSGAII performs better than other MOEAs in all scenarios. (C) 2018 Elsevier Inc. All rights reserved.

关键词： multiobjective optimization multiobjective evolutionary algorithms Vessel schedule recovery problem Maritime disruption management

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Population Classification in Fire Evacuation: A multiobjective Particle Swarm Optimization Approach

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2014年第1期18卷 70-81页

作者： Zheng, Yu-Jun Ling, Hai-Feng Xue, Jin-Yun Chen, Sheng-Yong Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China PLA Univ Sci & Technol Coll Field Engn Nanjing 210007 Jiangsu Peoples R China Jiangxi Normal Univ Jiangxi Prov Lab High Performance Comp Nanchang 330022 Peoples R China

In an emergency evacuation operation, accurate classification of the evacuee population can provide important information to support the responders in decision making;and therefore, makes a great contribution in protecting the population from potential harm. However, real-world data of fire evacuation is often noisy, incomplete, and inconsistent, and the response time of population classification is very limited. In this paper, we propose an effective multiobjective particle swarm optimization method for population classification in fire evacuation operations, which simultaneously optimizes the precision and recall measures of the classification rules. We design an effective approach for encoding classification rules, and use a comprehensive learning strategy for evolving particles and maintaining diversity of the swarm. Comparative experiments show that the proposed method performs better than some state-of-the-art methods for classification rule mining, especially on the real-world fire evacuation dataset. This paper also reports a successful application of our method in a real-world fire evacuation operation that recently occurred in China. The method can be easily extended to many other multiobjective rule mining problems.

关键词： Classification rules data mining fire evacuation multiobjective evolutionary algorithms particle swarm optimization

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A review of multiobjective test problems and a scalable test problem toolkit

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2006年第5期10卷 477-506页

作者： Huband, Simon Hingston, Phil Barone, Luigi While, Lyndon Edith Cowan Univ Mt Lawley WA 6050 Australia Univ Western Australia Crawley WA 6009 Australia

When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test problems employed in the EA literature have not been rigorously analyzed, which makes it difficult to draw accurate conclusions about the strengths and weaknesses of the algorithms tested on them. In this paper, we systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems. To support this, we first introduce a set of test problem criteria, which are in turn supported by a set of definitions. Our analysis of test problems highlights a number of areas requiring attention. Not only are many test problems poorly constructed but also the important class of nonseparable problems, particularly nonseparable multimodal problems, is poorly represented. Motivated by these findings, we present a flexible toolkit for constructing well-designed test problems. We also present empirical results demonstrating how the toolkit can be used to test an optimizer in ways that existing test suites do not.

关键词： evolutionary algorithms (EAs) multiobjective evolutionary algorithms multiobjective optimization multiobjective test problems

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Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm

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INFORMATION PROCESSING & MANAGEMENT 2006年第3期42卷 615-632页

作者： Cordón, O Herrera-Viedma, E Luque, M Univ Granada Dept Comp Sci & AI Granada Spain Univ Cordoba Dept Comp Sci & NA Cordoba Spain

The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall. A desirable aspect of IRSs, especially of those based on the IQBE paradigm, is to be able to get more than one query for the same information needs, with high precision and recall values or with different trade-offs between both. In this contribution, a new IQBE process is proposed combining a previous basic algorithm to automatically derive Boolean queries for Boolean IRSs [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] and an advanced evolutionary multiobjective approach [Coello, C. A., Van Veldhuizen, D. A., & Lamant, G. B. (2002). evolutionary algorithms for solving multiobjective problems. Kluwer Academic Publishers], which obtains several queries with a different precision-recall trade-off in a single run. The performance of the new proposal will be tested on the Cranfield and CACM collections and compared to the well-known Smith and Smith's algorithm, showing how it improves the learning of queries and thus it could better assist the user in the query formulation process. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： Boolean information retrieval systems genetic programming inductive query by example multiobjective evolutionary algorithms query learning

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Performance enhancement of multiobjective evolutionary optimisers for truss design using an approximate gradient

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COMPUTERS & STRUCTURES 2012年 106卷 115-124页

作者： Pholdee, Nantiwat Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen 40002 Thailand

This paper proposes hybridisation of evolutionary algorithms (EAs) and an efficient search strategy for truss optimisation. During an optimisation process, function gradients are approximated using already explored design solutions. The approximate gradient is then employed as a local search direction. The approximate gradient operator is integrated into the main search procedure of three multiobjective evolutionary algorithms (MOEAs) leading to three hybrid optimisers. The proposed hybrid strategies along with their original MOEAs are implemented on multiobjective design of truss structures. From the comparative results, it is found that the approximate gradient operator can greatly improve the search performance of MOEAs. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Hybrid algorithm Approximate gradient Truss structural optimisation multiobjective evolutionary algorithms

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VSD-MOEA: A Dominance-Based multiobjective evolutionary Algorithm with Explicit Variable Space Diversity Management

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evolutionary COMPUTATION 2022年第2期30卷 195-219页

作者： Chacon Castillo, Joel Segura, Carlos Coello Coello, Carlos A. Ctr Res Math CIMAT Comp Sci Dept CallejOn Jalisco S-N Guanajuato 36240 Guanajuato Mexico IPN CINVESTAV Dept Comp Sci Mexico City 07300 DF Mexico Basque Ctr Appl Math BCAM Madrid Spain Ikerbasque Madrid Spain

Most state-of-the-art multiobjective evolutionary algorithms (moeas) promote the preservation of diversity of objective function space but neglect the diversity of decision variable space. The aim of this article is to show that explicitly managing the amount of diversity maintained in the decision variable space is useful to increase the quality of moeas when taking into account metrics of the objective space. Our novel Variable Space Diversity-based MOEA (vsd-moea) explicitly considers the diversity of both decision variable and objective function space. This information is used with the aim of properly adapting the balance between exploration and intensification during the optimization process. Particularly, at the initial stages, decisions made by the approach are more biased by the information on the diversity of the variable space, whereas it gradually grants more importance to the diversity of objective function space as the evolution progresses. The latter is achieved through a novel density estimator. The new method is compared with state-of-art moeas using several benchmarks with two and three objectives. This novel proposal yields much better results than state-of-the-art schemes when considering metrics applied on objective function space, exhibiting a more stable and robust behavior.

关键词： multiobjective evolutionary algorithms premature convergence diversity preservation

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A New multiobjective evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2014年第1期18卷 54-69页

作者： Martin, Diana Rosete, Alejandro Alcala-Fdez, Jesus Herrera, Francisco Higher Polytech Inst JA Echeverria Dept Artificial Intelligence & Infrastruct Inform Havana 19390 Cuba Univ Granada CITIC UGR Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain King Abdulaziz Univ Fac Comp & Informat Technol North Jeddah Jeddah 21589 Saudi Arabia

Most of the algorithms for mining quantitative association rules focus on positive dependencies without paying particular attention to negative dependencies. The latter may be worth taking into account, however, as they relate the presence of certain items to the absence of others. The algorithms used to extract such rules usually consider only one evaluation criterion in measuring the quality of generated rules. Recently, some researchers have framed the process of extracting association rules as a multiobjective problem, allowing us to jointly optimize several measures that can present different degrees of trade-off depending on the dataset used. In this paper, we propose MOPNAR, a new multiobjective evolutionary algorithm, in order to mine a reduced set of positive and negative quantitative association rules with low computational cost. To accomplish this, our proposal extends a recent multiobjective evolutionary algorithm based on decomposition to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule, while introducing an external population and a restarting process to store all the nondominated rules found and to improve the diversity of the rule set obtained. Moreover, this proposal maximizes three objectives-comprehensibility, interestingness, and performance-in order to obtain rules that are interesting, easy to understand, and provide good coverage of the dataset. The effectiveness of the proposed approach is validated over several real-world datasets.

关键词： Data mining MOEA/D-DE multiobjective evolutionary algorithms negative association rules quantitative association rules

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