675 research outputs found

    Incorporating Memory and Learning Mechanisms Into Meta-RaPS

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    Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics. Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning mechanisms into Meta-RaPS, which is currently classified as a memoryless metaheuristic, can help the algorithm produce higher quality results. The proposed Meta-RaPS versions were created by taking different perspectives of learning. The first approach taken is Estimation of Distribution Algorithms (EDA), a stochastic learning technique that creates a probability distribution for each decision variable to generate new solutions. The second Meta-RaPS version was developed by utilizing a machine learning algorithm, Q Learning, which has been successfully applied to optimization problems whose output is a sequence of actions. In the third Meta-RaPS version, Path Relinking (PR) was implemented as a post-optimization method in which the new algorithm learns the good attributes by memorizing best solutions, and follows them to reach better solutions. The fourth proposed version of Meta-RaPS presented another form of learning with its ability to adaptively tune parameters. The efficiency of these approaches motivated us to redesign Meta-RaPS by removing the improvement phase and adding a more sophisticated Path Relinking method. The new Meta-RaPS could solve even the largest problems in much less time while keeping up the quality of its solutions. To evaluate their performance, all introduced versions were tested using the 0-1 Multidimensional Knapsack Problem (MKP). After comparing the proposed algorithms, Meta-RaPS PR and Meta-RaPS Q Learning appeared to be the algorithms with the best and worst performance, respectively. On the other hand, they could all show superior performance than other approaches to the 0-1 MKP in the literature

    An overview of population-based algorithms for multi-objective optimisation

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    In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided

    Ant colony optimisation and local search for bin-packing and cutting stock problems

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    The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

    A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks

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    We consider the problem of optimally designing a body wireless sensor network, while taking into account the uncertainty of data generation of biosensors. Since the related min-max robustness Integer Linear Programming (ILP) problem can be difficult to solve even for state-of-the-art commercial optimization solvers, we propose an original heuristic for its solution. The heuristic combines deterministic and probabilistic variable fixing strategies, guided by the information coming from strengthened linear relaxations of the ILP robust model, and includes a very large neighborhood search for reparation and improvement of generated solutions, formulated as an ILP problem solved exactly. Computational tests on realistic instances show that our heuristic finds solutions of much higher quality than a state-of-the-art solver and than an effective benchmark heuristic.Comment: This is the authors' final version of the paper published in G. Squillero and K. Sim (Eds.): EvoApplications 2017, Part I, LNCS 10199, pp. 1-17, 2017. DOI: 10.1007/978-3-319-55849-3\_16. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-55849-3_1

    A Membrane-Inspired Evolutionary Algorithm with a Population P System and its Application to Distribution System Recon guration

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    This paper develops a membrane-inspired evolutionary algorithm, PSMA, which is designed by using a population P system and a quantum-inspired evolutionary algorithm (QIEA). We use a population P system with three cells to organize three types of QIEAs, where communications between cells are performed at the level of genes, instead of the level of individuals reported in the existing membrane algorithms in the literature. Knapsack problems are applied to discuss the parameter setting and to test the effectiveness of PSMA. Experimental results show that PSMA is superior to four representative QIEAs and our previous work with respect to the quality of solutions and the elapsed time. We also use PSMA to solve the optimal distribution system reconfiguration problem in power systems for minimizing the power loss.Junta de AndalucĂ­a P08-TIC-04200Ministerio de Ciencia e InnovaciĂłn TIN-2009-1319

    Dynamic Network Formation Using Ant Colony Optimization

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    Military and industry are moving toward every device being network enabled and connected for reliable availability of communication and information. To make this type of system a reality, the devices must be capable of forming a network topology on their own in a dynamic environment to ensure that the correct information reaches a desired location and on-time. This research presents three contributions for solving highly dynamic (i.e. drastic change within the network) Multi-commodity Capacitated Network Design Problems (MCNDPs) resulting in a distributed multi-agent network design algorithm. The first contribution incorporates an Ant Colony Optimization (ACO) algorithm Ant Colony System (ACS) to solve the static MCNDP with weak constraints. Second, a new algorithm is developed and has the capability to dynamically adjust its exploration parameter of the solution space. This enhanced algorithm converges quickly and automatically adjusts to the dynamically changing network environment. Third, a distributed approach is created replacing the previous centralized solver. The distributed algorithm produces comparable results, but more importantly calculates the network topology in less than 20 percent of the computation time
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