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Artificial immune systems as means for inductive construction of optimum models for complex objects

JOURNAL OF AUTOMATION AND INFORMATION SCIENCES 2008年第6期40卷 41-59页

作者： Lytvynenko, V. I. Kherson National Technical University Ukraine

We described mechanisms of negative and clonal selection of biological systems, as well as basic notions of theory of immune network. On the basis of immunologic metaphors we gave formal representation of algorithms of clonal selection and immune network. Starting from the idea of soft calculations relative to integration of different paradigms for amplification of desirable computational properties of algorithms we adduced results of integration of external criterion, which is used in group method of data handling, ways of coding decisions and mutation operators, which are used in genes expression programming. We suggested the developed hybrid algorithms of clonal selection and immune network, which are intended for solving problems of inductive modeling.

关键词： mechanisms of negative and clonal selection biological systems theory of immune network algorithms clonal selection immune network integration of external criterion group method of data handling coding genes expression programming hybrid algorithms problems of inductive modeling

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Construct, Merge, Solve & Adapt A new general algorithm for combinatorial optimization

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COMPUTERS & OPERATIONS RESEARCH 2016年 68卷 75-88页

作者： Blum, Christian Pinacho, Pedro Lopez-Ibanez, Manuel Lozano, Jose A. Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence San Sebastian Spain Basque Fdn Sci IKERBASQUE Bilbao Spain Univ Santo Tomas Escuela Informat Concepcion Chile Univ Manchester Alliance Manchester Business Sch Manchester M13 9PL Lancs England

This paper describes a general hybrid metaheuristic for combinatorial optimization labelled Construct, Merge, Solve & Adapt. The proposed algorithm is a specific instantiation of a framework known from the literature as Generate-And-Solve, which is based on the following general idea. First, generate a reduced sub-instance of the original problem instance, in a way such that a solution to the sub-instance is also a solution to the original problem instance. Second, apply an exact solver to the reduced sub-instance in order to obtain a (possibly) high quality solution to the original problem instance. And third, make use of the results of the exact solver as feedback for the next algorithm iteration. The minimum common string partition problem and the minimum covering arborescence problem are chosen as test cases in order to demonstrate the application of the proposed algorithm. The obtained results show that the algorithm is competitive with the exact solver for small to medium size problem instances, while it significantly outperforms the exact solver for larger problem instances. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Metaheuristics Exact solver hybrid algorithms Minimum common string partition Minimum covering arborescence

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hybrid ant colony optimization algorithm applied to the multi-depot vehicle routing problem

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NATURAL COMPUTING 2020年第2期19卷 463-475页

作者： Stodola, Petr Univ Def Brno Czech Republic

The article deals with the hybrid Ant Colony Optimization algorithm and its application to the Multi-Depot Vehicle Routing Problem (MDVRP). The algorithm combines both probabilistic and exact techniques. The former implements the bio-inspired approach based on the behaviour of ants in the nature when searching for food together with simulated annealing principles. The latter complements the former. The algorithm explores the search space in a finite number of iterations. In each iteration, the deterministic local optimization process may be used to improve the current solution. Firstly, the key parts and features of the algorithm are presented, especially in connection with the exact optimization process. Next, the article deals with the results of experiments on MDVRP problems conducted to verify the quality of the algorithm;moreover, these results are compared to other state-of-the-art methods. As experiments, Cordreau's benchmark instances were used. The experiments showed that the proposed algorithm overcomes the other methods as it has the smallest average error (the difference between the found solution and the best known solution) on the entire set of benchmark instances.

关键词： Ant colony optimization Local optimization process Multi-depot vehicle routing problem hybrid algorithms Bio-inspired algorithms

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A new grouping genetic algorithm for clustering problems

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第10期39卷 9695-9703页

作者： Agustin-Blas, L. E. Salcedo-Sanz, S. Jimenez-Fernandez, S. Carro-Calvo, L. Del Ser, J. Portilla-Figueras, J. A. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain Tecnalia Res & Innovat Bizkaia Spain

In this paper we present a novel grouping genetic algorithm for clustering problems. Though there have been different approaches that have analyzed the performance of several genetic and evolutionary algorithms in clustering, the grouping-based approach has not been, to our knowledge, tested in this problem yet. In this paper we fully describe the grouping genetic algorithm for clustering, starting with the proposed encoding, different modifications of crossover and mutation operators, and also the description of a local search and an island model included in the algorithm, to improve the algorithm's performance in the problem. We test the proposed grouping genetic algorithm in several experiments in synthetic and real data from public repositories, and compare its results with that of classical clustering approaches, such as K-means and DBSCAN algorithms, obtaining excellent results that confirm the goodness of the proposed grouping-based methodology. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Grouping genetic algorithms Clustering problems hybrid algorithms

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Scheduling arc maintenance jobs in a network to maximize total flow over time

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DISCRETE APPLIED MATHEMATICS 2014年第PART 1期163卷 34-52页

作者： Boland, Natashia Kalinowski, Thomas Waterer, Hamish Zheng, Lanbo Univ Newcastle Sch Math & Phys Sci Callaghan NSW 2308 Australia

We consider the problem of scheduling a set of maintenance jobs on the arcs of a network so that the total flow over the planning time horizon is maximized. A maintenance job causes an arc outage for its duration, potentially reducing the capacity of the network. The problem can be expected to have applications across a range of network infrastructures critical to modern life. For example, utilities such as water, sewerage and electricity all flow over networks. Products are manufactured and transported via supply chain networks. Such networks need regular, planned maintenance in order to continue to function. However the coordinated timing of maintenance jobs can have a major impact on the network capacity lost due to maintenance. Here we describe the background to the problem, define it, prove it is strongly NP-hard, and derive four local search-based heuristic methods. These methods integrate exact maximum flow solutions within a local search framework. The availability of both primal and dual solvers, and dual information from the maximum flow solver, is exploited to gain efficiency in the algorithms. The performance of the heuristics is evaluated on both randomly generated instances, and on instances derived from real-world data. These are compared with a state-of-the-art integer programming solver. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Network flows Scheduling Maintenance planning Local search hybrid algorithms

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Combining Constraint Programming and Local Search for Job-Shop Scheduling

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INFORMS JOURNAL ON COMPUTING 2011年第1期23卷 1-14页

作者： Beck, J. Christopher Feng, T. K. Watson, Jean-Paul Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Sandia Natl Labs Discrete Math & Complex Syst Dept Albuquerque NM 87185 USA

Since their introduction, local search algorithms have consistently represented the state of the art in solution techniques for the classical job-shop scheduling problem. This dominance is despite the availability of powerful search and inference techniques for scheduling problems developed by the constraint programming community. In this paper, we introduce a simple hybrid algorithm for job-shop scheduling that leverages both the fast, broad search capabilities of modern tabu search algorithms and the scheduling-specific inference capabilities of constraint programming. The hybrid algorithm significantly improves the performance of a state-of-the-art tabu search algorithm for the job-shop problem and represents the first instance in which a constraint programming algorithm obtains performance competitive with the best local search algorithms. Furthermore, the variability in solution quality obtained by the hybrid is significantly lower than that of pure local search algorithms. Beyond performance demonstration, we perform a series of experiments that provide insights into the roles of the two component algorithms in the overall performance of the hybrid.

关键词： scheduling tabu search constraint programming hybrid algorithms

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Pattern classification using smallest normalized difference associative memory

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PATTERN RECOGNITION LETTERS 2017年 93卷 104-112页

作者： Ramirez-Rubio, Rogelio Aldape-Perez, Mario Yanez-Marquez, Cornelio Lopez-Yanez, Itzama Camacho-Nieto, Oscar Inst Politecn Nacl Ctr Innovac & Desarrollo Tecnol Computo IPN CIDET Av Juan de Dios BatizCIDETEC Bldg Mexico City 07738 DF Mexico Inst Politecn Nacl Ctr Invest Computac Av Juan de Dios BatizCIC Bldg Mexico City 07738 DF Mexico

In this paper a new associative classification algorithm is presented. The proposed algorithm overcomes the limitations of the original Alpha-Beta associative memory, while maintaining the fundamental set recalling capacity. This algorithm has two phases. The first phase is based on an Alpha-Beta auto-associative memory, which works in the domain of real numbers, unlike the traditional Alpha-Beta associative memories. In the second phase, normalized difference between the results of first phase and every pattern of the fundamental set is calculated. In order to demonstrate the behaviour and accuracy of the algorithm, multiple well known datasets and classification algorithms have been used. Experimental results have shown that our proposal achieved the best performance in three of the eight pattern classification problems in the medical field, using Stratified 10 Fold cross-validation. Our proposal achieved the best classification accuracy averaged over the all datasets addressed in the present work. Experimental results and statistical significance tests, allow us to affirm that the proposed model is an efficient alternative to perform pattern classification tasks. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Associative memories Decision support systems Pattern classification hybrid algorithms Supervised machine learning

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DEMORS: A hybrid multi-objective optimization algorithm using differential evolution and rough set theory for constrained problems

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COMPUTERS & OPERATIONS RESEARCH 2010年第3期37卷 470-480页

作者： Santana-Quintero, Luis V. Hernandez-Diaz, Alfredo G. Molina, Julian Coello Coello, Carlos A. Caballero, Rafael Pablo Olavide Univ Dept Econ Quantitat Methods & Econ Hist Seville 41013 Spain CINVESTAV IPN Dept Comp Sci Mexico City 07360 DF Mexico Univ Malaga Dept Appl Econ Math E-29071 Malaga Spain

The aim of this paper is to show how the hybridization of a multi-objective evolutionary algorithm (MOEA) and a local search method based on the use of rough set theory is a viable alternative to obtain a robust algorithm able to solve difficult constrained multi-objective optimization problems at a moderate computational cost. This paper extends a previously published MOEA [Hernandez-Diaz AG, Santana-Quintero LV, Coello Coello C, Caballero R Molina J. A new proposal for multi-objective optimization using differential evolution and rough set theory. In: 2006 genetic and evolutionary computation conference (GECCO'2006). Seattle, Washington, USA: ACM Press: July 2006], which was limited to unconstrained multi-objective optimization problems. Here, the main idea is to use this sort of hybrid approach to approximate the Pareto front of a constrained multi-objective optimization problem while performing a relatively low number of fitness function evaluations. Since in real-world problems the cost of evaluating the objective functions is the most significant, our underlying assumption is that, by aiming to minimize the number of such evaluations, our MOEA can be considered efficient. As in its previous version, our hybrid approach operates in two stages: in the first one, a multi-objective version of differential evolution is used to generate an initial approximation of the Pareto front. Then, in the second stage, rough set theory is used to improve the spread and quality of this initial approximation. To assess the performance of our proposed approach, we adopt, on the one hand, a set of standard bi-objective constrained test problems and, on the other hand, a large real-world problem with eight objective functions and 160 decision variables. The first set of problems are solved performing 10,000 fitness function evaluations, which is a competitive value compared to the number of evaluations previously reported in the specialized literature for such problems. The real-wo

关键词： hybrid algorithms Multi-objective optimization Differential evolution Rough set theory

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Two Distributed algorithms for the Frequency Assignment Problem in the Field of Radio Broadcasting

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IEEE TRANSACTIONS ON BROADCASTING 2009年第2期55卷 223-229页

作者： Idoumghar, Lhassane Schott, Rene Univ Haute Alsace LMIA F-68093 Mulhouse France IECN F-54506 Vandoeuvre Les Nancy France LORIA F-54506 Vandoeuvre Les Nancy France

The frequency assignment problem involves the assignment of discrete frequencies to the transmitters of a radio network, such as a radio broadcasting network. Frequency separation is necessary to avoid interference by other transmitters to the signal received from the wanted transmitter at the reception region. Here, it is of major importance to minimize the interference while at the same time using the spectrum efficiently. In this paper we present two original distributed algorithms implemented on clusters of PCs used to solve the frequency assignment problem in the field of radio broadcasting. The first one is based on the island distributed [1] implementation of our hybrid genetic algorithm [2]. The second one uses a distributed cooperative Tabu Search. Experimental results show that our algorithms, applied to several instances given by TDF-C2R, lead to important time performance improvements.

关键词： Cooperative tabu search algorithm frequency assignment problem hybrid algorithms parallel genetic algorithms radio broadcasting

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Decomposition of a Multiobjective Optimization Problem into a Number of Simple Multiobjective Subproblems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2014年第3期18卷 450-455页

作者： Liu, Hai-Lin Gu, Fangqing Zhang, Qingfu Guangdong Univ Technol Guangzhou 510520 Guangdong Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it proposes MOEA/D-M2M, a new version of multiobjective optimization evolutionary algorithm-based decomposition. This proposed algorithm solves these subproblems in a collaborative way. Each subproblem has its own population and receives computational effort at each generation. In such a way, population diversity can be maintained, which is critical for solving some MOPs. Experimental studies have been conducted to compare MOEA/D-M2M with classic MOEA/D and NSGA-II. This letter argues that population diversity is more important than convergence in multiobjective evolutionary algorithms for dealing with some MOPs. It also explains why MOEA/D-M2M performs better.

关键词： Decomposition hybrid algorithms multiobjective optimization

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