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In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.

This paper investigates an oriented spanning tree (OST) based genetic algorithm (GA) for the multi-criteria shortest path problem (MSPP) as well as the multi-criteria constrained shortest path problem (MCSPP). By encoding a path as an OST, in contrast with the existing evolutionary algorithms (EA) for shortest path problem (SPP), the designed GA provides a ''search from a paths set to another paths set'' mutation mechanism such that both of its local search and global search capabilities are greatly improved. Because the possibility to find a feasible path in a paths set is usually larger than that of only one path is feasible, the designed GA has more predominance for solving MCSPPs. Some computational tests are presented and the test results are compared with those obtained by a recent EA of which the encoding approach and the ideas of evolution operators such as mutation and crossover are adopted in most of the existing EAs for shortest path problems. The test results indicate that the new algorithm is available for both of MSPP and MCSPP.

Traffic assignment is a key step in urban traffic planning, and efficient path set significantly affects stochastic user equilibrium (SUE) traffic assignment. Thereby, the main allocation algorithms of the Logit model are analyzed and some disadvantages of them are point out. The conception of efficient path set should consider the path's character and traffic demand. Thus, the conception of path effect degree is defined according to the degree of one path affecting traffic volumes on other paths, and the conception of efficient path set is proposed considering the traffic demand. Moreover, efficient path set searching method is designed by combining the improved Dial algorithm. At last, a numerical example is given and the results verify the efficiency of efficient path set conception

Based on the Lyapunov stabilization theorem, a new method of global chaos synchronization among three different structures chaotic systems is proposed under the framework of drive-response systems. Then this method is applied to secure communication through chaotic masking. Using three coupled identical systems, proposes a novel method of chaos encryption, after encrypting in the first two coupled systems, encrypt again in the later two coupled systems. In consideration of interfering Gauss white noise, the results of encoding and decoding are simulated and analyzed. The characteristics of noise are analyzed and then a de-noising method using wavelet transform is presented. With Matlab, the effects of signal de-noising could be demonstrated.

Networks provide a useful way for modeling real world problems and are extensively used in many different types of systems: communications, hydraulic, mechanical, electronic, and logistics. The transportation problem (TP) is known as one of the important network problems. When the TP problem is associated with additional fixed cost for establishing the facilities or fulfilling the demand of customers, then it is called fixed charge transportation problem (fcTP). This problem is one of the NP-hard (IE/OR) problems that are difficult to solve using traditional methods. This paper aims to show the application of spanning tree-based Genetic Algorithm (GA) approach for solving nonlinear fixed charge transportation problem. Our new idea lies on the GA representation that includes the feasibility criteria and repairing procedure for the chromosome. Several numerical experimental results are presented to show the effectiveness of the proposed method.

A matching of a graph is an independent subset of the edges set and a maximum matching is a matching with as many edges in it as possible. The maximum weighted matching problem is to find a maximum matching in a given graph such that the sum of the weights of the edges in it is maximum. In this paper, the concepts of expected maximum fuzzy weighted matching, the α-maximum fuzzy weighted matching and the most maximum fuzzy weighted matching are initialized. According to various decision criteria, the maximum fuzzy weighted matching problem is formulated as expected value model, chance-constrained programming and dependent-chance programming by using the credibility theory, and the crisp equivalents are also given. Furthermore, a hybrid genetic algorithm is designed for solving the proposed fuzzy programming models. Finally, a numerical example is given.

The assignment problem is to find the total costs optimal jobs assignment schedule where n jobs are allocated to n workers, and each worker receives exactly just one job, such that the total cost is optimal. The quadratic assignment problem with penalty takes three types of costs into consideration: direct cost, interactive cost an penalty. In this paper, the fuzzy quadratic assignment problem with penalty is formulated as expected value model, chance-constrained programming and dependent-chance programming according to various decision criteria, and the crisp equivalents are given. Furthermore, hybrid genetic algorithm is designed for solving the proposed fuzzy programming models.

Let G = (V, E) be a graph. For a function f : V → {−1, 1}, the weight of f is w(f) = Σv ∈ V f(v). For a vertex v in V, we define f[v] = Σu∈N[v] f(u). A signed dominating function of G is a function f : V → {−1, 1} such that f[v] ⩾ 1 for all v ∈ V. The signed domination number γs(G) of G is the minimum weight of a signed dominating function on G. A signed dominating function of a weight γs(G) we call a γs-function of G. In this paper, we study the signed domination problem of general graph, and obtain some lower bounds of the signed domination number of a graph, and show that these lower bounds are sharp, and extend a result in Dunbar et al. (1995).

In this paper, we present a hybrid genetic algorithm to solve the bicriteria transportation problem. we absorb the concept on spanning tree and adopt the Prufer number as it is capable of equally and uniquely representing all possible basic solutions. We designed the criterion which chromosomes can be always feasibly converted to a transportation tree. In order to improve the efficiency of evolutionary algorithm, the reduced cost for optimality of a solution was hybridized to genetic algorithm. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.

In order to better solve discrete 0-1 knapsack problems, a novel global-best harmony search algorithm with binary coding, called DGHS, is proposed. First, an initialization based on a greedy mechanism is employed to improve the initial solution quality in DGHS. Next, we present a novel improvisation process based on intuitive cognition of improvising a new harmony, in which the best harmony of harmony memory (HM) is used to guide the searching direction of evolution during the process of memory consideration, or else a harmony is randomly chosen from HM and then a discrete genetic mutation is done with some probability during the phase of pitch adjustment. Third, a two-phase repair operator is employed to repair an infeasible harmony vector and to further improve a feasible solution. Last, a new selection scheme is applied to decide whether or not a new randomly generated harmony is included into the HM. The proposed DGHS is evaluated on twenty knapsack problems with different scales and compared with other three metaheuristics from the literature. The experimental results indicate that DGHS is efficient, effective, and robust for solving difficult 0-1 knapsack problems.

In this paper, we present improved genetic algorithm for solving the fuzzy multiobjective solid transportation problem in which the coefficients of objective function are represented as fuzzy numbers. The ranking fuzzy numbers with integral value are used in the evaluation and selection. The proposed algorithm is incorporated with problem-specific knowledge and conductive to find out the set of nondominated points in the criteria space based on decision maker degree of optimism.

In this paper, we propose neural network approach for multicriteria solid transportation problems(STP). First we suggest a neural network architecture to solve single-objective STP according to augmented Lagrange multiplier method. Due to the massive computing unit-neurons and parallel mechanism of neural network approach can solve the large scale problem efficiently and optimal solution can be got. Then we transform the original multicriteria problem into a single objective problem by global criteria method and adopt the neural network approach to solve it. By this way we can get the satisfactory solution of the original problem. The procedure and efficiency of this approach are shown with numerical simulations.

In this paper, we present neural networks for solving multicriteria solid transportation problems. The original problem is transformed into an equivalent continuous problem from the continuous-time dynamic system and its optimal solution can be got. The procedure and efficiency of this approach are shown with numerical simulations.

In this paper, we introduce the neural networks for solving fuzzy shortest path problems. The penalization of the neural networks is realized after transforming into crisp shortest path model. The procedure and efficiency of this approach are shown with numerical simulations.

The proposed Genetic Algorithms (GAs) are incorporated with problem-specific knowledge and the decision maker's degree of optimism, and the criteria space approach for bicriteria linear program conductive to find out the nondominated extreme points in the criteria space.

Dimensionality reduction, the process of reduce the number of dimension of the original feature set, plays an important role in a wide variety of contexts such as classification, Prediction and clustering. It is common to introduce the dimensionality reduction prior to the subsequent data mining tasks in the classical static data. However, this aforehand option can not capture the essence of datum because of the inherent time variety and one-pass constraint of data stream. In this case, dimensionality reduction should interact with the evolution of stream data with time elapsed. We introduced the interaction between dimensionality reduction in the time decaying high dimensional stream environment and propose the on-line fractal dimensionality reduction technique. Our performance experiments over a number of real and synthetic data sets illustrate the effectiveness and efficiency provided by our approach.

In this paper, we present a genetic algorithm with a spanning tree representation for solving the bicriteria fixed charge transportation problem. First we consider the fixed charge transportation problem with a single objective function by the spanning tree-based genetic algorithm, and then extend this GA approach to solve the bicriteria problem. Due to the bicriteria program, the fitness function is constructed by dynamic scaling which normalizes the different data from the bicriteria region. The proposed genetic algorithm can find Pareto optimal solutions in the bicriteria space. Computational results will show the performance of the spanning tree-based genetic algorithm.

In this paper, we present an evolutionary program for solving the fuzzy MSTP (multicriteria solid transportation problem) in which the coefficients of objective function are represented as fuzzy numbers. The ranking fuzzy numbers with integral value are used in the evaluation and selection. The proposed algorithm is incorporated with problem-specific knowledge and conductive to find out the set of nondominated points in the criteria space based on decision maker's degree of optimism.

We present an evolutionary computation of the fuzzy mSTP (multicriteria solid transportation problem) in which the coefficients of the objective function are represented as fuzzy numbers. The ranking fuzzy numbers with integral value are used in the evaluation and selection. The proposed algorithm is incorporated with problem-specific knowledge and can find the set of nondominated points in the criteria space based on decision maker degree of optimism.

In this paper, we present a new approach which is spanning tree-based genetic algorithm for solving a multi-objective transportation problem. The transportation problem as a special type of the network optimization problems has the special data structure in solution characterized as a transportation graph. In encoding transportation problem, we introduce one of node encodings based on a spanning tree which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation were designed based on this encoding. Also we designed the criterion that chromosome has always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy with (µ+λ)-selection and roulette wheel selection is used. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.