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This paper aims to improve premature convergence and low precision of the differential evolution algorithm. An adaptive constrained differential evolution algorithm based on a probability interval update mechanism (PIMDE) is proposed. First, the interval probability update mechanism is developed to adaptively generate the mutation probability value, and the hybrid mutation strategy is formulated by effectively selecting complementary mutation strategies based on the mutation probability. Subsequently, the adaptive mechanism is employed to dynamically adjust the mutation strategy and parameters; as a result, the global detection and local search ability of the algorithm can be balanced, and the search efficiency of the algorithm can be enhanced. To verify determine the performance of the proposed algorithm, ten standard constrained optimization problems and five engineering optimization problems are analyzed. As revealed from experimental results, PIMDE can effectively solve COPs with high precision and robustness. [ABSTRACT FROM AUTHOR]

In the practice of multi-school bus route planning, the characteristics of the bus fleet and the operation modes of buses have produced a variety of bus planning scenarios. In recent years, many methods have been used to arrange the routes of school buses for the school bus routing problem (SBRP) with a specific planning scenario. However, it is still a challenging task to develop a general-purpose algorithm that can effectively apply to the bus route planning for a variety of bus planning scenarios. This paper tries to develop a hybrid iterated local search (ILS) metaheuristic algorithm for SBRP with multiple planning scenarios, which including homogenous or heterogeneous fleets, single load or mixed load operation modes. Within the framework of ILS, a variety of neighborhood structures are used to improve the initial solution as well as the routes generated in the process of local search are recorded. For heterogeneous SBRP, the fleet adjustment strategy based on route segments is also applied. In addition, the perturbation mechanism and the acceptance of worse solutions within a certain deviation range are adopted to enhance the diversity of solutions. Finally, the local best solution is further promoted by the set partitioning procedure (SP), which is modeled by the history routes in the local search process. The experiment results prove that the proposed algorithm is effective and it also outperforms the existing algorithms for multi-school SBRP. [ABSTRACT FROM AUTHOR]

With the increment in figuring advancements, Cloud computing has permitted the clients to get to assets from anyplace whenever and to pay for the assets based on pay-peruse. Cloud computing allocates the submitted tasks to virtual machine (VM) by dynamic allocation. With the bandwidth and network connectivity getting better, more number of users and organizations are moving to Cloud. All things considered, there are different difficulties that are looked by Cloud service provider. One of the most important challenges is of load balancing. With the number of users gradually increasing, load balancing has become more important. Numerous calculations have been proposed till now for load adjusting, where every one of it centres around the various boundaries. However, these techniques suffer from various issues like - stuck in native optima. None of the calculations has demonstrated to be totally proficient. To deal with the issues identified with existing meta-heuristic procedures, in this paper, a mutation based ant colony optimization load balancing algorithm is proposed. Here we focus on the mutation in Ant Colony Optimization (ACO) algorithm for efficiently balancing the load among various data centers to further develop the presentation boundaries like response time and while improving the overall fitness function. [ABSTRACT FROM AUTHOR]

Recently, intuitionistic fuzzy sets and numbers are widely interesting in the literature, and many types of Intuitionistic Fuzzy Numbers (IFN's) are studied and applied to different mathematical and real life problems. In this paper, an algorithm to approximate general IFN's by the n-Intuitionistic Polygonal Fuzzy Number (n-IPFN) is introduced. The approximation facilitates the calculations due to its linearity, not to mention its realisticity and flexibility. After that, a new method to rank IFN's is introduced and applied to the n-IPFN. Based on that, convenient arithmetic operations for n-IPFN's that preserve the ranking values are proposed and shown to satisfy the most important properties. As a result, defining a ranking equivalence relation gave a strong algebraic structure that is isomorphic to the real numbers set. Finally, some definitions are proposed for dealing with matrices, functions, equalities and inequalities with n-IPFN's. [ABSTRACT FROM AUTHOR]

Road traffic monitoring is important for intelligent transportation, and researchers have begun to focus on the detection of traffic events using acoustic information. In this paper, we apply model fusion to traffic acoustic event classification. First, an improved, two-channel convolutional neural network (CNN) model is proposed as the weak classifier for constructing the fusion model. The mel-cepstral feature and its first-order and second-order difference are selected as the input features. Six different input features are constructed after signal preprocessing and segmentation. Second, after training six different CNN models, the voting method and support vector machine (SVM) stacking method are used to construct the final fusion model. Experimental results demonstrate that the detection rate of traffic acoustic events reaches 95.1%, which is higher than that of traditional traffic detection algorithms. [ABSTRACT FROM AUTHOR]

The vehicle problem (VRP) is a typical optimization problem in logistics and transportation. The objective function is to find the shortest route distances visited by all vehicles originating from a central deport to travel customers, and the sum of deliveries of each vehicle should meet the capacity constraint. This problem belongs to NP hard problems, so it is not easy to resolve it with common methods. Ant colony optimization (ACO) has shown prominent performance for many practical applications. However, it is inclined to premature convergence. The paper offers a hybrid ACO&DE algorithm, which hybridizes ant colony optimization (ACO) with differential evolution (DE) for the VRP. The main feature of the ACO&DE can make full use of advantages of the ACO and DE algorithm to make up for its own weakness, i.e., the ACO has fast construction mechanism, and the DE can extend the search scope of the ACO. Moreover, to make the DE suitable for solving the VRP, both strategies of mutation operator and crossover operator have been redesigned to implement the discrete DE directly. In addition, to increase the solution diversity by expanding the search space, we present a new selection strategy with probabilistic mechanism to determine new target vectors in the next iteration. Meanwhile, 2-opt heuristic and 2-exchange neighborhood is embedded in the ACO&DE to improve the local search performance. The results have shown that the proposed ACO&DE algorithm is competitive with existing optimal methods in solving the VRP, and thus can be further extended in variants of the VRP and other logistics transportation fields. [ABSTRACT FROM AUTHOR]

The k-hypergeometric functions are defined as pFq(a, k, b, s; z) = ∑∞ n=0 (a1)n,k1 (a2)n,k2 ···(ap)n,kp z n (b1)n,s1 (b2)n,s2 ···(bq)n,sq n!, where (x)n,k = x(x + k)(x + 2k)· · ·(x + (n − 1)k) is the Pochhammer k-symbol. In this paper, efficient recursive algorithms for computing the parameter derivatives of the k-hypergeometric functions are developed. As the generalized hypergeometric functions are special cases of this function and many special functions can be expressed in terms of the generalized hypergeometric functions, the algorithms can also be extended to computing the parameter derivatives of the hypergeometric functions and many other special functions. The Bessel functions and modified Bessel functions are presented as examples of such an application. Theoretical analysis is worked out, some computation using Mathematica is performed, and data is provided to show the advantages of our algorithms. [ABSTRACT FROM AUTHOR]

This paper concentrates on studying the application of fuzzy soft sets and the construction of an algorithm to identify a better approach flood alarm prediction model that applies to eight selected provinces sites in Northern Thailand. Finally, an example is provided to show which of the methods can be successfully used to predict potential flood in the future. [ABSTRACT FROM AUTHOR]

In this paper, the quasi-optimal and optimal algorithms are considered for discriminating the pulse signals by arrival time in the presence of the fast Gaussian modulating interference and additive Gaussian white noise. The case is examined when the time positions of the discriminable signals are a priori unknown and they take values from different clock intervals, so that such signals do not overlap in time. For the introduced algorithms, the analytical expressions are obtained for both the first two moments of the decision statistics and the probabilities of errors occurring while discriminating the signals by arrival time. By statistical simulation methods, one confirms the good agreement of these expressions with the corresponding experimental data presenting a wide range of values of the parameters of both the useful signals and the modulating interference. The comparison of the performance of the quasioptimal and optimal algorithms in terms of the discrimination error probability is also carried out. It is established that the optimal algorithm can provide a significant gain in the discrimination quality. [ABSTRACT FROM AUTHOR]

In present paper, a new family of smoothing exact penalty functions is given for a general nonlinear inequality constrained optimization problem. We study the simple smoothed penalty algorithm and its convergenceis under the generalized MFCQ condition. [ABSTRACT FROM AUTHOR]