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Multi-objective memetic optimization for the bi-objective obnoxious p-median problem

KNOWLEDGE-BASED SYSTEMS 2018年 144卷 88-101页

作者： Colmenar, J. M. Marti, R. Duarte, A. Univ Rey Juan Carlos Dept Ciencias Comp Madrid 28933 Spain Univ Valencia Dept Estadist & Invest Operat E-46100 Valencia Spain

Location problems have been studied extensively in the optimization literature, the p-median being probably one of the most tackled models. The obnoxious p-median is an interesting variant that appears in the context of hazardous location. The aim of this paper is to formally introduce a bi-objective optimization model for this problem, in which a solution consists of a set of p locations, and two conflicting objectives arise. On the one hand, the sum of the minimum distance between each client and their nearest open facility and, on the other hand, the dispersion among facilities. Both objective values should be kept as large as possible for a convenient location of dangerous facilities. We propose a Multi-Objective memetic Algorithm (MOMA) to obtain high-quality approximations to the efficient front of the bi-objective obnoxious p-median problem, denoted as Bi-OpM. In particular, we introduce efficient crossover and mutation mechanisms. Additionally, we present several multi-objective local search methods. All the strategies are finally incorporated in a memetic algorithm which limits the search to the feasible region, thus performing an efficient exploration of the solutions space. Our experimentation compares several state-of-the-art procedures with the introduced MOMA emerging as the best performing method in all considered multi-objective metrics. (C) 2018 Elsevier B.V. All rights reserved.

关键词： memetic algorithms Local search Multi-objective optimization Combinatorial problems Obnoxious p-median

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A memetic Algorithm for Multi-Level Redundancy Allocation

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IEEE TRANSACTIONS ON RELIABILITY 2010年第4期59卷 754-765页

作者： Wang, Zai Tang, Ke Yao, Xin Univ Sci & Technol China Sch Comp Sci & Technol NICAL Hefei 230027 Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Redundancy allocation problems (RAPs) have attracted much attention for the past thirty years due to its wide applications in improving the reliability of various engineering systems. Because RAP is an NP-hard problem, and exact methods are only applicable to small instances, various heuristic and meta-heuristic methods have been proposed to solve it. In the literature, most studies on RAPs have been conducted for single-level systems. However, real-world engineering systems usually contain multiple levels. In this paper, the RAP on multi-level systems is investigated. A novel memetic algorithm (MA) is proposed to solve this problem. Two genetic operators, namely breadth-first crossover and breadth-first mutation, and a local search method are designed for the MA. Comprehensive experimental studies have shown that the proposed MA outperformed the state-of-the-art approach significantly on two representative examples.

关键词： Evolutionary algorithms memetic algorithms multi-level systems redundancy allocation

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Opposition-Based memetic Algorithm and Hybrid Approach for Sorting Permutations by Reversals

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EVOLUTIONARY COMPUTATION 2019年第2期27卷 229-265页

作者： Soncco-Alvarez, Jose Luis Munoz, Daniel M. Ayala-Rincon, Mauricio Univ Brasilia Dept Comp Sci BR-70910900 Brasilia DF Brazil Univ Brasilia Fac Gama Dept Mech Engn BR-72444240 Gama DF Brazil Univ Brasilia Fac Gama Dept Elect Engn BR-72444240 Gama DF Brazil Univ Brasilia Dept Math BR-70910900 Brasilia DF Brazil

Sorting unsigned permutations by reversals is a difficult problem;indeed, it was proved to be NP-hard by Caprara (1997). Because of its high complexity, many approximation algorithms to compute the minimal reversal distance were proposed until reaching the nowadays best-known theoretical ratio of 1.375. In this article, two memetic algorithms to compute the reversal distance are proposed. The first one uses the technique of opposition-based learning leading to an opposition-based memetic algorithm;the second one improves the previous algorithm by applying the heuristic of two breakpoint elimination leading to a hybrid approach. Several experiments were performed with one-hundred randomly generated permutations, single benchmark permutations, and biological permutations. Results of the experiments showed that the proposed OBMA and Hybrid-OBMA algorithms achieve the best results for practical cases, that is, for permutations of length up to 120. Also, Hybrid-OBMA showed to improve the results of OBMA for permutations greater than or equal to 60. The applicability of our proposed algorithms was checked processing permutations based on biological data, in which case OBMA gave the best average results for all instances.

关键词： Sorting by reversals genetic algorithms memetic algorithms opposition-based learning heuristics

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A memetic approach for training set selection in imbalanced data sets

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第11期10卷 3043-3070页

作者： Nikpour, Bahareh Nezamabadi-pour, Hossein Shahid Bahonar Univ Kerman Dept Elect Engn IDPL Kerman Iran Shahid Bahonar Univ Kerman Mahani Math Res Ctr Kerman Iran

Imbalanced data classification is a challenging problem in the field of machine learning. The problem occurs when data samples have an uneven distribution amongst the classes and classical classifiers are not suitable for classifying such datasets. To overcome this problem, in this paper, the best training samples are selected from data samples with the goal of improving the performance of classifier when dealing with imbalanced data. To do so, some heuristic methods are presented which use local information to give a proper view about whether removing or retaining each sample of training set. Subsequently, the methods are considered as local search algorithms and combined with a global search algorithm in a framework to form memetic algorithms. The global search used in this paper is binary quantum inspired gravitational search algorithm (BQIGSA) which is a new metaheuristic search for optimization of binary encoded problems. BQIGSA is employed since we seek for a highly stochastic and random search algorithm to solve our problem. We propose to use six different local search algorithms, three of which are application oriented that we designed based on the problem and the rest are general, and the best local search is then determined. Experiments are performed on 45 standard datasets, and G-mean and AUC criteria are considered as evaluation tools. Then, the data sets are employed to compare the best memetic approaches with some popular state of the art algorithms as well as a recently proposed memetic algorithm and the results show their superiority. At the last step, the performance of the proposed algorithm for four different classifiers is evaluated and the best classifier is determined to be utilized for this method.

关键词： Imbalanced data Under-sampling methods Training set selection Metaheuristics memetic algorithms Binary quantum-inspired gravitational search algorithm

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The Interleaved Constructive memetic Algorithm and its application to timetabling

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COMPUTERS & OPERATIONS RESEARCH 2012年第10期39卷 2310-2322页

作者： Ozcan, Ender Parkes, Andrew J. Alkan, Alpay Univ Nottingham Sch Comp Sci ASAP Res Grp Nottingham NG8 1BB England Yeditepe Univ Dept Comp Engn Istanbul Turkey

Timetabling problems are well known NP-hard constraint satisfaction problems, and real-world cases often have complicated and challenging structures. For such problems, we present a new hybrid method, "Interleaved Constructive memetic Algorithm" (ICMA) that interleaves memetic algorithms with constructive methods. ICMA works using an active subset of all the events. Starting with a few events, in multiple construction stages ICMA increases the active set to eventually include all of them. At each stage, a memetic algorithm (MA) is applied to improve the current partial solution before the next construction step. We also describe a real-world course timetabling problem, the "Preparation School Timetabling Problem" (PSTP), which is of particular interest because it has a highly hierarchical structure arising from various organisational requirements. An important advantage of ICMA is that both the constructive heuristics and MA can be tailored to exploit such hierarchical structures. We give empirical results showing that ICMA performs better than the corresponding conventional MA on the PSTP. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Genetic algorithms Timetabling Scheduling Metaheuristics memetic algorithms

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Integrating memetic search into the BioHEL evolutionary learning system for large-scale datasets

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memetic COMPUTING 2013年第2期5卷 95-130页

作者： Calian, Dan Andrei Bacardit, Jaume UCL Dept Comp Sci London C1E 6BT England Univ Nottingham Sch Comp Sci Interdisciplinary Comp & Complex Syst ICOS Res Gr Nottingham NG8 1BB England

Local search methods are widely used to improve the performance of evolutionary computation algorithms in all kinds of domains. Employing advanced and efficient exploration mechanisms becomes crucial in complex and very large (in terms of search space) problems, such as when employing evolutionary algorithms to large-scale data mining tasks. Recently, the GAssist Pittsburgh evolutionary learning system was extended with memetic operators for discrete representations that use information from the supervised learning process to heuristically edit classification rules and rule sets. In this paper we first adapt some of these operators to BioHEL, a different evolutionary learning system applying the iterative learning approach, and afterwards propose versions of these operators designed for continuous attributes and for dealing with noise. The performance of all these operators and their combination is extensively evaluated on a broad range of synthetic large-scale datasets to identify the settings that present the best balance between efficiency and accuracy. Finally, the identified best configurations are compared with other classes of machine learning methods on both synthetic and real-world large-scale datasets and show very competent performance.

关键词： memetic algorithms Evolutionary algorithms Evolutionary rule learning

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A competent memetic algorithm for complex scheduling

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NATURAL COMPUTING 2012年第1期11卷 151-160页

作者： Gonzalez, Miguel A. Vela, Camino R. Varela, Ramiro Univ Oviedo AI Ctr Oviedo Spain Univ Oviedo Dept Comp Sci Oviedo Spain

We face the job shop scheduling problem with sequence dependent setup times and makespan minimization by memetic algorithm. This algorithm combines a classic genetic algorithm with a local searcher. The performance of the local searcher relies on the combination of a tabu search algorithm with a neighborhood structure termed N (S) that are thoroughly described and analyzed. Also, two evolution models are considered: Lamarckian and Baldwinian evolution. We report results from an experimental study across conventional benchmark instances showing that the proposed algorithm outperforms the current state-of-the-art methods and that Lamarckian evolution is better than Baldwinian evolution.

关键词： memetic algorithms Tabu search Scheduling Setup times

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Optimal Design and Control of Smart Space Structures: A memetic Evolution Approach

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IEEE INTELLIGENT SYSTEMS 2014年第1期29卷 40-46页

作者： Li, Dong-Xu Xu, Rui Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha Hunan Peoples R China

To reach global optimal performance, here the authors propose a new design approach that offers concurrent design for smart space structures. In this approach, the quantity and placement of sensors/actuators and parameters of controllers are simultaneously optimized by a memetic evolution algorithm. A solar array smart structure has been used for computational experiments and the corresponding results indicate that the proposed concurrent design can obtain better performance than the sequential one.

关键词： Design Methodology Intelligent Sensors Aerospace Electronics Smart Buildings Intelligent Systems memetics Intelligent Systems memetic algorithms Optimal Design Smart Structures

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memetic algorithm with route decomposing for periodic capacitated arc routing problem

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APPLIED SOFT COMPUTING 2017年 52卷 1130-1142页

作者： Zhang, Yuzhou Mei, Yi Tang, Ke Jiang, Keqin Anqing Normal Univ Sch Comp & Informat Anqing 246133 Peoples R China Victoria Univ Wellington Sch Engn & Comp Sci Kelburn 6012 New Zealand Univ Sci & Technol China Sch Comp Sci & Technol USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230027 Peoples R China

In this paper,he Periodic Capacitated Arc Routing Problem (PCARP) is investigated. PCARP is an extensionof the well-known CARP from a single period to a multi-period horizon. In PCARP, two objectives are tobe minimized. One is the number of required vehicles (nv), and the other is the total cost (tc). Due to the multi-period nature, given the same graph or road network, PCARP can have a much larger solution space than the single-period CARP counterpart. Furthermore, PCARP consists of an additional allocation sub-problem (of the days to serve the arcs), which is interdependent with the routing sub-problem. Although some attempts have been made for solving PCARP, more investigations are yet to be done to further improve their performance especially on large-scale problem instances. It has been shown that optimizing nv and tc separately (hierarchically) is a good way of dealing with the two objectives. In this paper, we further improve this strategy and propose a new Route Decomposition (RD) operatorthereby. Then, the RD operator is integrated into a memetic Algorithm (MA) framework for PCARP, in which novel crossover and local search operators are designed accordingly. In addition, to improve the search efficiency, a hybridized initialization is employed to generate an initial population consisting of both heuristic and random individuals. The MA with RD (MARD) was evaluated and compared withthe state-of-the-art approaches on two benchmark sets of PCARP instances and a large data set which is based on a real-world road network. The experimental results suggest that MARD outperforms the compared state-of-the-art algorithms, and improves most of the best-known solutions. The advantage of MARD becomes more obvious when the problem size increases. Thus, MARD is particularly effective in solving large-scale PCARP instances. Moreover, the efficacy of the proposed RD operator in MARD has been empirically verified. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Periodic capacitated arc routing problem Combinatorial optimization Metaheuristics memetic algorithms

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A memetic algorithm for the flexible flow line scheduling problem with processor blocking

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COMPUTERS & OPERATIONS RESEARCH 2009年第2期36卷 402-414页

作者： Tavakkoli-Moghaddam, Reza Safaei, Nima Sassani, Farrokh Univ Tehran Fac Engn Dept Ind Engn Tehran Iran Univ Tehran Fac Engn Engn Optimizat Res Grp Tehran Iran Univ British Columbia Dept Mech Engn Vancouver BC V6T 1Z4 Canada Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada

This paper introduces an efficient memetic algorithm (MA) combined with a novel local search engine, namely, nested variable neighbourhood search (NVNS), to solve the flexible flow line scheduling problem with processor blocking (FFLB) and without intermediate buffers. A flexible flow line consists of several processing stages in series, with or without intermediate buffers, with each stage having one or more identical parallel processors. The line produces a number of different products, and each product must be processed by at most one processor in each stage. To obtain an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches and optimization tools is extremely difficult. Our proposed MA employs a new representation, operators, and local search method to solve the above-mentioned problem. The computational results obtained in experiments demonstrate the efficiency of the proposed MA, which is significantly superior to the classical genetic algorithm (CGA) under the same conditions when the population size is increased in the CGA. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： Flexible flow line Processor blocking memetic algorithms Variable neighbourhood search

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