290 research outputs found

    Metaheuristic approaches to virtual machine placement in cloud computing: a review

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    On the use of biased-randomized algorithms for solving non-smooth optimization problems

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    Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

    Solving Economic Dispatch Problem with Valve-Point Effect using a Modified ABC Algorithm

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    This paper presents a new approach for solving economic dispatch (ED) problem with valve-point effect using a modified artificial bee colony (MABC) algorithm. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. This paper proposes a novel best mechanism algorithm based on a modified ABC algorithm, in which a new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. To demonstrate the effectiveness of the proposed method, the numerical studies have been performed for two different sample systems. The results of the proposed method are compared with other techniques reported in recent literature. The results clearly show that the proposed MABC algorithm outperforms other state-of-the-art algorithms in solving ED problem with the valve-point effect.DOI:http://dx.doi.org/10.11591/ijece.v3i3.251

    Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques

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    yesThis paper introduces a novel symbolic regression approach, namely biogeographical-based programming (BBP), for the prediction of elastic modulus of self-compacting concrete (SCC). The BBP model was constructed directly from a comprehensive dataset of experimental results of SCC available in the literature. For comparison purposes, another new symbolic regression model, namely artificial bee colony programming (ABCP), was also developed. Furthermore, several available formulas for predicting the elastic modulus of SCC were assessed using the collected database. The results show that the proposed BBP model provides slightly closer results to experiments than ABCP model and existing available formulas. A sensitivity analysis of BBP parameters also shows that the prediction by BBP model improves with the increase of habitat size, colony size and maximum tree depth. In addition, among all considered empirical and design code equations, Leemann and Hoffmann and ACI 318-08’s equations exhibit a reasonable performance but Persson and Felekoglu et al.’s equations are highly inaccurate for the prediction of SCC elastic modulus

    Metaheuristics and Chaos Theory

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    Chaos theory is a novelty approach that has been widely used into various applications. One of the famous applications is the introduction of chaos theory into optimization. Note that chaos theory is highly sensitive to initial condition and has the feature of randomness. As chaos theory has the feature of randomness and dynamical properties, it is easy to accelerate the optimization algorithm convergence and enhance the capability of diversity. In this work, we integrated 10 chaotic maps into several metaheuristic algorithms in order to extensively investigate the effectiveness of chaos theory for improving the search capability. Extensive experiments have been carried out and the results have shown that chaotic optimization can be a very promising tool for solving optimization algorithms

    Hybrid biogeography-based evolutionary algorithms

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    Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The new hybrid EAs include two approaches to hybridization: (1) iteration-level hybridization, in which various EAs and BBO are executed in sequence; and (2) algorithm-level hybridization, which runs various EAs independently and then exchanges information between them using ideas from biogeography. Our empirical study shows that the new hybrid EAs significantly outperforms their constituent algorithms with the selected tuning parameters and generation limits, and algorithm-level hybridization is generally better than iteration-level hybridization. Results also show that the best new hybrid algorithm in this paper is competitive with the algorithms from the 2013 CEC competition. In addition, we show that the new hybrid EAs are generally robust to tuning parameters. In summary, the contribution of this paper is the introduction of biogeography-based hybridization strategies to the EA community

    An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

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    In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC
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