Search

Abstract: In this paper, a new hybrid genetic algorithm is proposed, which combines the genetic algorithm with hill-climbing search steps differently from some former algorithms. The new algorithm can be widely applied to a class of global optimization problems for continuous functions with box constraints. Finally, numerical examples show that this algorithm can yield the global optimum with high efficiency. [Copyright &y& Elsevier]

Abstract: The particle swarm optimization (PSO) has been widely used to solve continuous problems. The discrete problems have just begun to be also solved by the discrete PSO. However, the combinatorial problems remain a prohibitive area to the PSO mainly in case of integer values. In this paper, we propose a combinatorial PSO (CPSO) algorithm that we take up challenge to use in order to solve a multi-mode resource-constrained project scheduling problem (MRCPSP). The results that have been obtained using a standard set of instances, after extensive experiments, prove to be very competitive in terms of number of problems solved to optimality. By comparing average deviations and percentages of optima found, our CPSO algorithm outperforms the simulated annealing algorithm and it is close to the PSO algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, by using the unified procedures, an improved immune algorithm named a modified Taguchi-immune algorithm (MTIA), based on both the features of an artificial immune system and the systematic reasoning ability of the Taguchi method, is proposed to solve both the global numerical optimization problems with continuous variables and the combinatorial optimization problems for the job-shop scheduling problems (JSP). The MTIA combines the artificial immune algorithm, which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimal antibody. In the MTIA, the clonal proliferation within hypermutation for several antibody diversifications and the recombination by using the Taguchi method for the local search are integrated to improve the capabilities of exploration and exploitation. The systematic reasoning ability of the Taguchi method is executed in the recombination operations to select the better antibody genes to achieve the potential recombination, and consequently enhance the MTIA. The proposed MTIA is effectively applied to solve 15 benchmark problems of global optimization with 30 or 100 dimensions. The computational experiments show that the proposed MTIA can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing improved genetic algorithms reported recently in the literature. In addition, the MTIA is also applied to solve the famous Fisher-Thompson and Lawrence benchmarks of the JSP. The computational experiments show that the proposed MTIA approach can also obtain both better and more robust results than those evolutionary methods reported recently. [Copyright &y& Elsevier]

Abstract: In this paper, we deal with the maximum diversity problem (MDP), which asks to select a specified number of elements from a given set so that the sum of distances between the selected elements is as large as possible. We develop an iterated tabu search (ITS) algorithm for solving this problem. We also present a steepest ascent algorithm, which is well suited in application settings where solutions of satisfactory quality are required to be provided very quickly. Computational results for problem instances involving up to 5000 elements show that the ITS algorithm is a very attractive alternative to the existing approaches. In particular, we demonstrate the outstanding performance of ITS on the MDP instances taken from the literature. For 69 such instances, new best solutions were found. [Copyright &y& Elsevier]

Abstract: Ant algorithm is a multi-agent systems inspired by the behaviors of real ant colonies function to solve optimization problems. In this paper an ant colony optimization algorithm is developed to solve the quadratic assignment problem. The local search process of the algorithm is simulated annealing. In the exploration of the search space, the evaluation of pheromones which are laid on the ground by ants is used. In this work, the algorithm is analyzed by using current problems in the literature and is compared with other metaheuristics. [Copyright &y& Elsevier]

Abstract: Over the past few years, researchers have developed a number of multiobjective evolutionary algorithms (MOEAs). Although most studies concentrate on solving unconstrained optimization problems, there exit a few studies where MOEAs have been extended to solve constrained optimization problems. Most of them were based on penalty functions for handling nonlinear constraints by genetic algorithms. However the performance of these methods is highly problem-dependent, many methods require additional tuning of several parameters. In this paper, we present a new optimization algorithm, which is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. The algorithm maintains a finite-sized archive of nondominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. The use of ε-dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto set approximation by choosing an appropriate ε value, which guarantees convergence and diversity. The results, provided by the proposed algorithm for six benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems. [Copyright &y& Elsevier]

Abstract: Although statistical modeling is a common task in different fields of science, it is still difficult to estimate the best model that can accurately describe inherent characteristics of a system for which historical or experimental data are available. Since we may classify estimating techniques as optimizations, we can model this problem as an optimization problem and solve it by a new heuristic algorithm like neural networks, genetic algorithms, and tabu search or by classic ones such as regression and econometric models. In this paper, we propose a new type of genetic algorithm to find the best regression model among all suggested and evaluate its performances by an economical case study. [Copyright &y& Elsevier]

Abstract: Stochastic searching methods have been applied widely to areas such as continuous and combinatorial optimization problems in a number of disciplines. Many existing methods solve these problems by navigating on the surface of the possibly rugged landscape. This kind of navigation is not very effective because the property of the landscape at different resolutions can be very different. Time spent at the beginning of the search on the detailed part of the landscape is often useless. Appropriate searching strategies should be adopted at different resolutions. In this paper, we propose a new probabilistic searching model for global optimization. The main contributions of the model are (1) to provide a basis for resolution control and smoothing of search space and (2) to introduce continuous memory into stochastic search. The basis of resolution control is achieved by dividing the search space into a finite number of n-dimensional partitions structurally. The number of partitions governs the resolution of the search space. The more the partitions, the finer is the search space and the more detailed and rugged is the landscape. The benefits are twofold. First, the rugged landscape problem can be smoothed, because the ruggedness is a matter of the number of partitions. Hence, the difficulty in search due to the ruggedness of the landscape can be controlled. Second, it provides a basis to implement algorithms that may change the ‘view’ of the landscape during the search process because we can dynamically divide the search space accordingly. Another important feature that we use is continuous memory. Throughout the search process, searching experience is continuously accumulated in order to shape the global picture of the search space guiding the future searching direction. We present results on the algorithm performance in handling numerical function optimization. The empirical results show that our new model is comparable to, and in many cases performs better than, that of the other advanced methods in terms of solution quality and computation required. [Copyright &y& Elsevier]