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Hyper-spherical inversion transformations in multi-objective evolutionary optimization

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期177卷 1678-1702页

作者： Large, J. W. Jones, D. F. Tamiz, M. Univ Portsmouth Dept Math Portsmouth PO1 3HE Hants England

multi-objective evolutionary algorithms (MOEAs) are widely considered to have two goals: convergence towards the true Pareto front and maintaining a diverse set of solutions. The primary concern here is with the first goal of convergence, in particular when one or more variables must converge to a constant value. Using a number of well known test problems, the difficulties that are currently impeding convergence are discussed and then a new method is proposed that transforms the decision space using the geometric properties of hyper-spherical inversions to converge towards/onto the true Pareto front. Future extensions of this work and its application to multi-objective optimisation is discussed. (c) 2005 Elsevier B.V. All rights reserved.

关键词： inversion transformation crossover convergence multi-objective evolutionary algorithms

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A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems

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APPLIED INTELLIGENCE 2012年第2期36卷 330-347页

作者： Jose Gacto, Maria Alcala, Rafael Herrera, Francisco Univ Jaen Dept Comp Sci Intelligent Syst & Data Min Res Grp Jaen Spain Univ Granada Dept Comp Sci & Artificial Intelligence Soft Comp & Intelligent Informat Syst Res Grp Granada Spain

This paper focuses on the use of multi-objective evolutionary algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems, energy performance, stability and indoor comfort requirements. This problem presents some specific restrictions that make it very particular and complex because of the large time requirements needed to consider multiple criteria (which enlarge the solution search space) and the long computation time models required in each evaluation. In this work, a specific multi-objective evolutionary algorithm is proposed to obtain more compact fuzzy logic controllers as a way of finding the best combination of rules, thus improving the system performance to better solve the HVAC system control problem. This method combines lateral tuning of the linguistic variables with rule selection. To this end, two objectives have been considered, maximizing the performance of the system and minimizing the number of rules obtained. This algorithm is based on the well-known SPEA2 but uses different mechanisms for guiding the search towards the desired Pareto zone. Moreover, the method implements some advanced concepts such as incest prevention, that help to improve the exploration/exploitation trade-off and consequently its convergence ability. The proposed method is compared to the most representative mono-objective steady-state genetic algorithms previously applied to the HVAC system control problem, and to generational and steady-state versions of the most interesting multi-objective evolutionary algorithms (never applied to this problem) showing that the solutions obtained by this new approach dominate those obtained by these methods. The results obtained confirm the effectiveness of our approach compared with the rest of the analyzed methods, obtaining more accurate fuzzy logic controllers with simpler models.

关键词： Heating, ventilating, and air conditioning systems HVAC systems Fuzzy logic controllers Genetic tuning Linguistic 2-tuples representation Rule selection multi-objective evolutionary algorithms

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An optimal hybrid adaptive controller based on the multi-objective evolutionary algorithm for an under-actuated nonlinear ball and wheel system

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2020年第4期34卷 1723-1734页

作者： Pezhman, Alireza Rezapour, Javad Mahmoodabadi, Mohammad Javad Islamic Azad Univ Dept Mech Engn Lahijan Branch Lahijan Iran Sirjan Univ Technol Dept Mech Engn Sirjan Iran

The present paper introduces an optimal hybrid adaptive robust sliding mode control based on a combination of feedback linearization and a PD controller (ARSMC-PD) to present a general scheme for stabilization of a ball and wheel system. Since the combination of adaptive sliding mode and feedback linearization cannot reduce the control efforts and tracking error, a PD controller is added to enhance the performance and provide sufficient optimal control input. At first, an adaptive mechanism is used to update the parameters of the SMC controller according to the gradient descent method and the chain rule of differentiation to minimize the sliding surface. Then, the strength Pareto evolutionary algorithm (SPEA-II) is utilized to minimize the control effort and the integral time absolute errors of the system response simultaneously in the presence of disturbance. The results prove the efficiency of the proposed controller in two aspects of low tracking error and optimal control input.

关键词： Sliding mode controller Feedback linearization PD controller multi-objective evolutionary algorithms Gradient descent method Ball and wheel system

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A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第2期24卷 277-293页

作者： Cheshmehgaz, Hossein Rajabalipour Desa, Mohamad Ishak Wibowo, Antoni UTM Fac Comp Sci & Informat Syst Skudai 81310 Johor Malaysia

Nowadays, time and cost are familiar criteria for every logistic provider, and both have been long treated to be minimized simultaneously. However, the criteria are naturally conflicted even with flexibilities and/or constraints appeared in the logistic networks. This paper is concerned with three-level logistic networks with potential suppliers, distributed centers (DCs), and deterministic demands from available consumers. The networks also benefit from potential direct shipments from suppliers to consumers as long as suppliers and DCs facilities might face limited capacity in their own seasonal supplying and warehousing processes. The goal is (re)configure the networks in order to minimize response time to consumers, transportation cost and facility cost. Therefore, the networks are formulated as multiple criteria decision making problems, which have more than one configuration through the time and cost optimizing at the same time. Due to the flexibility and the constraints, the decision maker(s) needs a set of compromise solutions for the networks that represent optimal configurations based on the objectives without considering prior knowledge. To this end, the problems are formulated into four individual logistic network models varying with the flexibility option and/or the capacitated facilities. To find the compromise solutions, Pareto-based multi-objective evolutionary algorithm, NSGA-II is customized and then utilized to deal with an illustrative case study. The results are analyzed through the two performance measures, hypervolume and the number of optimal solutions obtained so far.

关键词： Flexible logistic network model multi-objective optimization Pareto solutions multi-objective evolutionary algorithms

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A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems

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SOFT COMPUTING 2019年第4期23卷 1321-1336页

作者： Elhag, Salma Fernandez, Alberto Altalhi, Abdulrahman Alshomrani, Saleh Herrera, Francisco Univ Jeddah Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

Intrusion detection systems are devoted to monitor a network with aims at finding and avoiding anomalous events. In particular, we focus on misuse detection systems, which are trained to identify several known types of attacks. These can be unauthorized accesses, or denial of service attacks, among others. Whenever it scans a trace of a suspicious event, it is programmed to trigger an alert and/or to block this dangerous access to the system. Depending on the security policies of the network, the administrator may seek different requirements that will have a strong dependency on the behavior of the intrusion detection system. For a given application, the cost of raising false alarms could be higher than carrying out a preventive access lock. In other scenarios, there could be a necessity of correctly identifying the exact type of cyber attack to proceed in a given way. In this paper, we propose a multi-objective evolutionary fuzzy system for the development of a system that can be trained using different metrics. By increasing the search space during the optimization of the model, more accurate solutions are expected to be obtained. Additionally, this scheme allows the final user to decide, among a broad set of solutions, which one is better suited for the current network characteristics. Our experimental results, using the well-known KDDCup'99 problem, supports the quality of this novel approach in contrast to the state-of-the-art for evolutionary fuzzy systems in intrusion detection, as well as the C4.5 decision tree.

关键词： Intrusion detection systems Computational intelligence evolutionary fuzzy systems multi-objective evolutionary algorithms Misuse detection

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A multiobjective evolutionary approach for linearly constrained project selection under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期179卷 869-894页

作者： Medaglia, Andres L. Graves, Samuel B. Ringuest, Jeffrey L. Univ Los Andes Dept Ingn Ind Bogota Colombia Univ Los Andes Ctr Optimizac & Probabilida Aplicada Bogota Colombia Boston Coll Operat Informat & Strateg Management Dept Chestnut Hill MA 02167 USA

In the project selection problem a decision maker is required to allocate limited resources among an available set of competing projects. These projects could arise, although not exclusively, in an R&D, information technology or capital budgeting context. We propose an evolutionary method for project selection problems with partially funded projects, multiple (stochastic) objectives, project interdependencies (in the objectives), and a linear structure for resource constraints. The method is based on posterior articulation of preferences and is able to approximate the efficient frontier composed of stochastically nondominated solutions. We compared the method with the stochastic parameter space investigation method (PSI) and illustrate it by means of an R&D portfolio problem under uncertainty based on Monte Carlo simulation. (c) 2005 Elsevier B.V. All rights reserved.

关键词： multi-objective optimization project selection multi-objective evolutionary algorithms

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A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第4期36卷 8255-8268页

作者： Lau, H. C. W. Chan, T. M. Tsui, W. T. Chan, F. T. S. Ho, G. T. S. Choy, K. L. Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Pokfulam Hong Kong Peoples R China

In the field Of Supply chain management and logistics, using vehicles to deliver products from suppliers to customers is one of the major operations. Before transporting products, optimizing the routing of vehicles is required so as to provide a low-cost and efficient service for customers. This paper deals with the problem of optimization of vehicle routing in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time constraint, the objective considered in this paper comprises not only the total traveling distance, but also the total traveling time. We propose using a multi-objective evolutionary algorithm called the fuzzy logic guided non-dominated sorting genetic algorithm 2 (FL-NSGA2) to solve this multi-objective optimization problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after ten consecutive generations. In order to demonstrate the effectiveness of FL-NSGA2, we compared it with the following: non-dominated sorting genetic algorithms 2 (NSGA2) (without the guide of fuzzy logic), strength Pareto evolutionary algorithm 2 (SPEA2) (with and without the guide of fuzzy logic), and micro-genetic algorithm (MICROGA) (with and without the guide of fuzzy logic). Simulation results showed that FL-NSGA2 outperformed other search methods in all of three various scenarios. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Supply chain management Logistics Vehicle routing problem multi-objective optimization multi-objective evolutionary algorithms Genetic algorithms Fuzzy logic

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multi-objective evolutionary feature selection for online sales forecasting

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NEUROCOMPUTING 2017年 234卷 75-92页

作者： Jimenez, F. Sanchez, G. Garcia, J. M. Sciavicco, G. Miralles, L. Univ Murcia Fac Comp Sci Madrid Spain Univ Ferrara Dept Math & Comp Sci Ferrara Italy Univ Panamericana Fac Ingn Campus Mexico Augusto Rodin 498 Mexico City 03920 Mexico

Sales forecasting uses historical sales figures, in association with products characteristics and peculiarities, to predict short-term or long-term future performance in a business, and it can be used to derive sound financial and business plans. By using publicly available data, we build an accurate regression model for online sales forecasting obtained via a novel feature selection methodology composed by the application of the multi objective evolutionary algorithm ENORA (evolutionary NOn-dominated Radial slots based Algorithm) as search strategy in a wrapper method driven by the well-known regression model learner Random Forest. Our proposal integrates feature selection for regression, model evaluation, and decision making, in order to choose the most satisfactory model according to an a posteriori process in a multi-objective context. We test and compare the performances of ENORA as multi-objective evolutionary search strategy against a standard multi objective evolutionary search strategy such as NSGA-11 (Non-dominated Sorted Genetic Algorithm), against a classical backward search strategy such as RFE (Recursive Feature Elimination), and against the original data set.

关键词： multi-objective evolutionary algorithms Feature selection Random forest Regression model Online sales forecasting

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Project scheduling: A multi-objective evolutionary algorithm that optimizes the effectiveness of human resources and the project makespan

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ENGINEERING OPTIMIZATION 2013年第1期45卷 45-65页

作者： Yannibelli, Virginia Amandi, Analia UNCPBA Fac Cs Exactas ISISTAN Res Inst Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn CONICET Buenos Aires DF Argentina

In this article, the project scheduling problem is addressed in order to assist project managers at the early stage of scheduling. Thus, as part of the problem, two priority optimization objectives for managers at that stage are considered. One of these objectives is to assign the most effective set of human resources to each project activity. The effectiveness of a human resource is considered to depend on its work context. The other objective is to minimize the project makespan. To solve the problem, a multi-objective evolutionary algorithm is proposed. This algorithm designs feasible schedules for a given project and evaluates the designed schedules in relation to each objective. The algorithm generates an approximation to the Pareto set as a solution to the problem. The computational experiments carried out on nine different instance sets are reported.

关键词： project scheduling human resource assignment multi-skilled resources effectiveness of human resources multi-objective evolutionary algorithms

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objective space division-based hybrid evolutionary algorithm for handing overlapping solutions in combinatorial problems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第4期19卷 3369-3401页

作者： Gonzalez, Begona Rossit, Daniel A. Mendez, Maximo Frutos, Mariano Univ Las Palmas de Gran Canaria ULPGC Inst Univ SIANI Las Palmas Gran Canaria Spain Univ Nacl Sur Engn Dept INMABB UNS CONICET Bahia Blanca Buenos Aires Argentina Univ Nacl Sur Engn Dept ILESS UNS CONICET Bahia Blanca Buenos Aires Argentina

Overlapping solutions occur when more than one solution in the space of decisions maps to the same solution in the space of objectives. This situation threatens the exploration capacity of multi objective evolutionary algorithms (MOEAs), preventing them from having a good diversity in their population. The influence of overlapping solutions is intensified on multi-objective combinatorial problems with a low number of objectives. This paper presents a hybrid MOEA for handling overlapping solutions that combines the classic NSGA-II with a strategy based on objective Space Division (OSD). Basically, in each generation of the algorithm, the objective space is divided into several regions using the nadir solution calculated from the current generation solutions. Furthermore, the solutions in each region are classified into non-dominated fronts using different optimization strategies in each of them. This significantly enhances the achieved diversity of the approximate front of non-dominated solutions. The proposed algorithm (called NSGA-II/OSD) is tested on a classic Operations Research problem: the multi-objective Knapsack Problem (0-1 MOKP) with two objectives. Classic NSGA-II, MOEA/D and Global WASF-GA are used to compare the performance of NSGA-II/OSD. In the case of MOEA/D two different versions are implemented, each of them with a different strategy for specifying the reference point. These MOEA/D reference point strategies are thoroughly studied and new insights are provided. This paper analyses in depth the impact of overlapping solutions on MOEAs, studying the number of overlapping solutions, the number of solution repairs, the hypervolume metric, the attainment surfaces and the approximation to the real Pareto front, for different sizes of 0-1 MOKPs with two objectives. The proposed method offers very good performance when compared to the classic NSGA-II, MOEA/D and Global WASF-GA algorithms, all of them well-known in the literature.

关键词： objective space division overlapping solutions multi-objective evolutionary algorithms multi-objective combinatorial optimization problems bi-objective knapsack problem

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