Search

Compared with single-layer networks, multiplex networks can describe real-world scenarios in more detail while suffering from requiring considerable computing and storage resources at the same time. Network feature learning, which aims to embed networks into a low dimensional space, is an effective method for solving these problems. Currently, research on multiplex network embedding faces two major challenges: how to make full use of the connected information in different layers and how to embed multiplex networks into a unified space. In this paper, a novel multiplex network embedding model is proposed to solve these two problems. It preserves all the first-, second- and multi-order proximities in multiplex networks by optimizing the corresponding objective functions. The network reconstruction step combines information of different types of relations in other layers while maintaining their distinctive properties. The proposed synchronous learning strategy provides a path to embed multiplex networks into a unified space. Extensive experiments on three real applications: visualization, link prediction and node classification are conducted to validate the effectiveness of the proposed method. The experimental results show that it achieves better or comparable performance compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The water wave optimization (WWO) algorithm is inspired by shallow water wave theory and mainly simulates propagation, refraction and breaking to obtain the global optimal solution in the search space. Due to its premature convergence and low optimization efficiency, the basic WWO has a slow convergence speed and low calculation accuracy. To improve the overall optimization performance of the basic WWO, an enhanced WWO based on the elite opposition-based learning strategy and the simplex method (ESWWO) is proposed to solve the function optimization problem and path planning problem for an autonomous underwater vehicle (AUV). The elite opposition-based learning strategy increases the diversity of the population and enhances the global search ability to avoid falling into the local optimum. The simplex method has a fast search speed and strong local search ability to obtain a very accurate solution. The ESWWO algorithm can not only achieve complementary advantages to improve the optimization efficiency of the basic WWO but can also balance exploration and exploitation to obtain the global optimal solution. For the function optimization problem, the ESWWO has strong stability and robustness, and the fitness values of the ESWWO are better than those of other algorithms. For the AUV path planning problem, the ESWWO can avoid threat areas with a minimum fuel cost to obtain the optimal path. The experimental results show that the overall optimization performance of the ESWWO algorithm is superior to that of other algorithms, and thus, ESWWO is an effective and feasible method for solving the function optimization problem and AUV path planning problem. [ABSTRACT FROM AUTHOR]

Abstract The moth swarm algorithm (MSA) is a recent swarm intelligence optimization algorithm, but its convergence precision and ability can be limited in some applications. To enhance the MSA's exploration abilities, an enhanced MSA called the elite opposition-based MSA (EOMSA) is proposed. For the EOMSA, an elite opposition-based strategy is used to enhance the diversity of the population and its exploration ability. The EOMSA was validated using 23 benchmark functions and three structure engineering design problems. The results show that the EOMSA can find a more accurate solution than other population-based algorithms, and it also has a fast convergence speed and high degree of stability. [ABSTRACT FROM AUTHOR]

A new speech processing algorithm is proposed to improve speech intelligibility in noisy environments without increasing speech energy. The method improves the near-end speech intelligibility by optimizing the frame-based spectral energy correlation between clean speech and noisy modified speech with a power constraint. This algorithm is developed based on short-time objective intelligibility (STOI) measure, which predicts speech intelligibility in background noise according to the correlation of clean and noisy speeches. The proposed method is compared with unprocessed speech and two baseline methods using two objective intelligibility measures and an intelligibility listening test under various noisy conditions. Results show large intelligibility improvements with the proposed method over the unprocessed noisy speech. In addition, compared with the baseline methods, the proposed algorithm provides the best intelligibility scores for all noisy conditions in the STOI measure and for low signal-to-noise ratios in the speech intelligibility index. The word recognition results also show that the proposed algorithm performs better than the unprocessed speech and the reference methods. An objective quality measure is applied to investigate the speech quality of the introduced method, which is proven to insignificantly affect speech quality. [ABSTRACT FROM AUTHOR]

In this paper, an improved quantum-behaved particle swarm optimization (CL-QPSO), which adopts a new collaborative learning strategy to generate local attractors for particles, is proposed to solve nonlinear numerical problems. Local attractors, which directly determine the convergence behavior of particles, play an important role in quantum-behaved particle swarm optimization (QPSO). In order to get a promising and efficient local attractor for each particle, a collaborative learning strategy is introduced to generate local attractors in the proposed algorithm. Collaborative learning strategy consists of two operators, namely orthogonal operator and comparison operator. For each particle, orthogonal operator is used to discover the useful information that lies in its personal and global best positions, while comparison operator is used to enhance the particle’s ability of jumping out of local optima. By using a probability parameter, the two operators cooperate with each other to generate local attractors for particles. A comprehensive comparison of CL-QPSO with some state-of-the-art evolutionary algorithms on nonlinear numeric optimization functions demonstrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is a simple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing exploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability Ps. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the selfadaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to enhance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic oppositionbased learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark functions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms. [ABSTRACT FROM AUTHOR]

In this study, a new single-solution based metaheuristic, namely the Vortex Search (VS) algorithm, is proposed to perform numerical function optimization. The proposed VS algorithm is inspired from the vortex pattern created by the vortical flow of the stirred fluids. To provide a good balance between the explorative and exploitative behavior of a search, the proposed method models its search behavior as a vortex pattern by using an adaptive step size adjustment scheme. The proposed VS algorithm is tested over 50 benchmark mathematical functions and the results are compared to both the single-solution based (Simulated Annealing, SA and Pattern Search, PS) and population-based (Particle Swarm Optimization, PSO2011 and Artificial Bee Colony, ABC) algorithms. A Wilcoxon-Signed Rank Test is performed to measure the pair-wise statistical performances of the algorithms, the results of which indicate that the proposed VS algorithm outperforms the SA, PS and ABC algorithms while being competitive with the PSO2011 algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimization method inspired by a paradigm from nature is introduced. Chemical reactions are used as a paradigm to propose an algorithm that can be considered as a general purpose optimization technique. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. [ABSTRACT FROM AUTHOR]

Abstract: The research on optimal design of infinite-impulse response (IIR) filters based on optimization techniques has gained much attention in recent years. However, due to the limited performance of the applied optimization techniques, the orders of the filters, which can be obtained, are very low in the previous research. Memetic algorithms (MAs) are widely recognized to have better convergence capability than their conventional counterparts. However, the universality of the MAs, e.g. the ability of solving diverse kinds of digital IIR filter designs, is still limited. In this paper, we design a Two-Stage ensemble Memetic Algorithm (TSMA) framework to more appropriately synthesize the strengths of the evolutionary global search and local search techniques. In the first optimization stage, a competition is held among the candidate local search techniques. Its major idea is to choose the best local search technique and to obtain good initial state. Inheriting the good information of the first stage, the second optimization stage is to implement effective adaptive MA to pursue high-quality solution. The experimental studies presented in this paper contain three aspects: (1) the benefits of the TSMA framework are experimentally investigated by comparing TSMA with its sub-optimizers and recent effective evolutionary algorithms (EAs) on 26 test functions; then (2) TSMA is compared with 4 MAs on the CEC05 functions to comprehensively show the advantages of TSMA; and (3) the TSMA and 6 state-of-the-art algorithms are applied to design high-order digital infinite-impulse response (IIR) filters. The experimental results definitely demonstrate the excellent effectiveness, efficiency and reliability of TSMA on both function optimization and digital IIR filter design tasks. [Copyright &y& Elsevier]

Abstract: This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify the trajectories of particles to avoid being trapped in the local optimum. With two different ways of integration of CA and PSO, two versions of CPSO, i.e. CPSO-inner and CPSO-outer, have been discussed. For the former, we devised three typical lattice structures of CA used as neighborhood, enabling particles to interact inside the swarm; and for the latter, a novel CA strategy based on “smart-cell” is designed, and particles employ the information from outside the swarm. Theoretical studies are made to analyze the convergence of CPSO, and numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on benchmark test functions. [Copyright &y& Elsevier]