The design of DNA sequences is essential for the implementation of DNA computing, where the quantity and quality can directly affect the accuracy and efficiency of calculations. Many studies have focused on the design...
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The design of DNA sequences is essential for the implementation of DNA computing, where the quantity and quality can directly affect the accuracy and efficiency of calculations. Many studies have focused on the design of good DNA sequences to make DNA computing more reliable. However, DNA sequence design needs to satisfy various constraints at the same time, which is an NP-hard problem. In this study, we specify appropriate constraints that should be satisfied in the design of DNA sequences and we propose evaluation formulae. We employ the invasiveweedoptimization (IWO) algorithm and the niche crowding in the algorithm to solve the DNA sequence design problem. We also improve the spatial dispersal in the traditional IWO algorithm. Finally, we compared the sequences obtained with existing sequences based on the results obtained using a comprehensive fitness function, which demonstrated the efficiency of the proposed method.
Given a set of n cities and m salesmen stationed at d depots, the fixed destination multidepot salesmen problem consists in finding tours for all the salesmen which start and end at their corresponding fixed depots su...
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Given a set of n cities and m salesmen stationed at d depots, the fixed destination multidepot salesmen problem consists in finding tours for all the salesmen which start and end at their corresponding fixed depots such that each city is visited exactly once by one salesman only, the workload among salesmen is balanced and the total distance traveled by all the salesmen is minimized. In this paper, we have proposed two swarm intelligence approaches for this problem. The first approach is based on artificial bee colony algorithm, whereas the latter approach is based on invasive weed optimization algorithm. Computational results on instances derived from TSPLIB show the effectiveness of our proposed approaches over other state-of-the-art approaches for this problem.
The multiple traveling salesperson problem (MTSP) is similar to famous traveling salesperson problem (TSP) except for the fact that there are more than one salesperson to visit the cities though each city must be visi...
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The multiple traveling salesperson problem (MTSP) is similar to famous traveling salesperson problem (TSP) except for the fact that there are more than one salesperson to visit the cities though each city must be visited exactly once by only one salesperson. For this problem, we have considered two different objectives. First one is to minimize the total distance traveled by all the salespersons, whereas the second one is to minimize the maximum distance traveled by anyone salesperson. This latter objective is about fairness as it tries to balance the workload among salespersons. MTSP, being a generalization of TSP under both the objectives, is also NP-Hard. In this paper, we have proposed two metaheuristic approaches for the MTSP. The first approach is based on artificial bee colony algorithm, whereas the second approach is based on invasive weed optimization algorithm. We have also applied a local search to further improve the solution obtained through our approaches. Computational results on a wide range of benchmark instances show the superiority of our proposed approaches over all the other state-of-the-art approaches for this problem on both the objectives. (C) 2014 Elsevier B.V. All rights reserved.
In this study, some different approaches were designed, implemented, and evaluated to perform multi-criteria route planning by considering a driver's preferences in multi-criteria route selection. At first, by usi...
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In this study, some different approaches were designed, implemented, and evaluated to perform multi-criteria route planning by considering a driver's preferences in multi-criteria route selection. At first, by using a designed neuro-fuzzy toolbox, the driver's preferences in multi-criteria route selection such as the preferred criteria in route selection, the number of route-rating classes, and the routes with the same rate were received. Next, to learn the driver's preferences in multi-criteria route selection and to classify any route based on these preferences, a methodology was proposed using a locally linear neuro-fuzzy model (LLNFM) trained with an incremental tree based learning algorithm. In this regard, the proposed LLNFM-based methodology reached better results for running-times, as well as root mean square error (RMSE) estimations in learning and testing processes of training/checking data-set in comparison with those of the proposed adaptive neuro-fuzzy inference system (ANFIS) based methodology. Finally, the trained LLNFM-based methodology was utilized to plan and predict a driver's preferred routes by classifying Pareto-optimal routes obtained by running the modified invasiveweedoptimization (IWO) algorithm between an origin and a destination of a real urban transportation network based on the driver's preferences in multi-criteria route selection. (C) 2014 Elsevier Ltd. All rights reserved.
The invasive weed optimization algorithm (IWO) is an optimization method inspired by dynamic growth of weeds colony. The authors of the present paper have modified the IWO algorithm introducing a hybrid strategy of th...
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ISBN:
(纸本)9783642293528;9783642293535
The invasive weed optimization algorithm (IWO) is an optimization method inspired by dynamic growth of weeds colony. The authors of the present paper have modified the IWO algorithm introducing a hybrid strategy of the search space exploration. The goal of the project was to evaluate the modified version by testing its usefulness for numerical functions minimization. The optimized multidimensional functions: Griewank, Rastrigin, and Rosenbrock are frequently used as benchmarks which allows to compare the experimental results with outcomes reported in the literature. Both the results produced by the original version of the IWO algorithm and the Adaptive Particle Swarm optimization (APSO) method served as the reference points.
Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solution...
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ISBN:
(纸本)9781424450534
Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solutions in optimization problems, for which traditional mathematical techniques may fail. This paper does a comparative study of results of five evolutionary algorithms: Genetic algorithm (GA), Particle Swarm optimization (PSO) algorithm, Artificial Bee Colony (ABC) algorithm, invasiveweedoptimization (IWO) algorithm and Artificial Immune (AI) algorithm when applied to some standard benchmark multivariable functions.
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