A new adaptive neural network and heuristics hybrid approach for job-shop scheduling

Copyright @ 2001 Elsevier Science LtdA new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided.This work is supported by the National Nature Science Foundation (No. 69684005) and National High -Tech Program of P. R. China (No. 863-511-9609-003)

Solving the problem of optimizing wind farm design using genetic algorithms

Renewable energies have become a topic of great interest in recent years because the natural sources used for the generation of these energies are inexhaustible and non-polluting. In fact, environmental sustainability requires a considerable reduction in the use of fossil fuels, which are highly polluting and unsustainable [1]. In addition, serious environmental pollution is threatening human health, and many public concerns have been raised [2]. As a result, many countries have proposed ambitious plans for the production of green energy, including wind power, and consequently, the market for wind energy is expanding rapidly worldwide [3]. In this research, an evolutionary metaheuristic is implemented, specifically genetic algorithms

Simulated annealing and Tabu search in the long run: A comparison on QAP tasks☆

AbstractSimulated Annealing (SA) and Tabu Search (TS) are compared on the Quadratic Assignment Problem. A recent work on the same benchmark suite argued that SA could achieve a reasonable solution quality with fewer function evaluations than TS. The discussion is extended by showing that the conclusions must be changed if the task is hard or a very good approximation of the optimal solution is desired, or if CPU time is the relevant parameter. In addition, a recently proposed version of TS (the Reactive Tabu Search) solves the problem of finding the proper list size with an automatic memory-based reaction mechanism

A genetic algorithm for shortest path with real constraints in computer networks

The shortest path problem has many different versions. In this manuscript, we proposed a muti-constrained optimization method to find the shortest path in a computer network. In general, a genetic algorithm is one of the common heuristic algorithms. In this paper, we employed the genetic algorithm to find the solution of the shortest path multi-constrained problem. The proposed algorithm finds the best route for network packets with minimum total cost, delay, and hop count constrained with limited bandwidth. The new algorithm was implemented on four different capacity networks with random network parameters, the results showed that the shortest path under constraints can be found in a reasonable time. The experimental results showed that the algorithm always found the shortest path with minimal constraints

Track-oriented multiple hypothesis tracking based on Tabu search and Gibbs sampling

In order to circumvent the curse of dimensionality in multiple hypothesis tracking data association, this paper proposes two efficient implementation algorithms using Tabu search and Gibbs sampling. As the first step, we formulate the problem of generating the best global hypothesis in multiple hypothesis tracking as the problem of finding a maximum weighted independent set of a weighted undirected graph. Then, the metaheuristic Tabu search with two basic movements is designed to find the global optimal solution of the problem formulated. To improve the computational efficiency, this paper also develops a sampling based algorithm based on Gibbs sampling. The problem formulated for the Tabu search-based algorithm is reformulated as a maximum product problem to enable the implementation of Gibbs sampling. The detailed algorithm is then designed and the convergence is also theoretically analyzed. The performance of the two algorithms proposed are verified through numerical simulations and compared with that of a mainstream multiple dimensional assignment implementation algorithm. The simulation results confirm that the proposed algorithms significantly improve the computational efficiency while maintaining or even enhancing the tracking performance

Feature Selection for Very Short-Term Heavy Rainfall Prediction Using Evolutionary Computation

We developed a method to predict heavy rainfall in South Korea with a lead time of one to six hours. We modified the AWS data for the recent four years to perform efficient prediction, through normalizing them to numeric values between 0 and 1 and undersampling them by adjusting the sampling sizes of no-heavy-rain to be equal to the size of heavy-rain. Evolutionary algorithms were used to select important features. Discriminant functions, such as support vector machine (SVM), k-nearest neighbors algorithm (k-NN), and variant k-NN (k-VNN), were adopted in discriminant analysis. We divided our modified AWS data into three parts: the training set, ranging from 2007 to 2008, the validation set, 2009, and the test set, 2010. The validation set was used to select an important subset from input features. The main features selected were precipitation sensing and accumulated precipitation for 24 hours. In comparative SVM tests using evolutionary algorithms, the results showed that genetic algorithm was considerably superior to differential evolution. The equitable treatment score of SVM with polynomial kernel was the highest among our experiments on average. k-VNN outperformed k-NN, but it was dominated by SVM with polynomial kernel

Генетичний алгоритм з жадібним стохастичним оператором схрещування для передбачення третинної структури білка

Розроблено новий генетичний алгоритм, особливістю якого є запропонований жадібний стохастичний оператор схрещування. Застосування пропонованого алгоритму досліджується на задачі передбачення третинної структури білка. Наведено результати обчислювального експерименту.Цель работы. Описание генетического алгоритма с новым жадным стохастическим оператором скрещивания. В сравнении предлагаемого алгоритма с лучшими известными имплементациями генетических и миметических алгоритмов, используемых для определения пространственной структуры белка. Результат. Работа предлагаемого алгоритма сравнивается с другими на базе 10 известных цепей длиной 48, для которых найден глобальный минимум свободной энергии, впервые предложенных в [13]. Алгоритм нашел 9 из 10 пространственных структур, на которых достигается глобальный минимум свободной энергии, а также продемонстрировал лучшее среднее значение решений, чем алгоритмы, с которыми он сравнивался.The purpose of the article is to describe a genetic algorithm with a new greedy stochastic crossover operator, reveal its advantages and disadvantages, compare the proposed algorithm with the best-known implementations of genetic and memetic algorithms for the spatial protein structure prediction, and make conclusions with future steps suggestion afterward. Result. The work of the proposed algorithm is compared with others on the basis of 10 known chains with a length of 48 first proposed in [13]. For each of the chain, a global minimum of free energy was already pre-calculated. The algorithm found 9 out of 10 spatial structures on which a global minimum of free energy is achieved and also demonstrated a better average value of solutions than the comparing algorithms