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Weapon target allocation (WTA) is a classic NP-complete problem in the field of military operations research. In this paper, we addressed the multi-constraint WTA problems in multilayer defense scenario. To solve large-scale WTA problems effectively, a distributed MAX-MIN Ant System (MMAS) algorithm based on distributed computing framework Spark was developed and improved. An experiment environment comprising virtual machines was built for implementing the distributed MMAS. First, a small-scale WTA example, whose theoretical optimal solution can be obtained by existing optimization software, was taken as a benchmark problem to assess the performance of distributed MMAS. The result shows that it can find high-quality and robust approximate solutions. Then a large-scale WTA problem was constructed and used to further evaluate the performance of distributed MMAS in the experiment environment. The result shows that the distributed MMAS can also achieve high-quality approximate solutions with high robustness and computational efficiency even for large scale WTA problems. Our study demonstrates it is a promising approach for solving large-scale iteration-dependent optimization problems like WTA by means of incorporating heuristic optimization algorithms such as Ant Colony Optimization into distributed computing framework. [ABSTRACT FROM AUTHOR]

In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a problem of optimization on the set of either matrices or vectors. Briefly, SLRA is defined as follows. Given an initial matrix with a certain structure (for example, Hankel), the aim is to find a matrix of specified lower rank that approximates this initial matrix, whilst maintaining the initial structure. We demonstrate that the optimization problem arising is typically very difficult; in particular, the objective function is multiextremal even in simple cases. We also look at different methods of solving the SLRA problem. We show that some traditional methods do not even converge to a locally optimal matrix. [ABSTRACT FROM AUTHOR]

Reinforcement learning is hard to solve optimization problems in multi-agent system because of the inefficiency of function approximation. Sparse distributed memories, which is implemented using Radial Basis Functions or Kanerva Coding, can be used to improve the efficiency. But this approach still often gives poor performance when applied to large-scale multi-agent systems. In this paper, we attempt to solve four-rooms problem in the predator-prey pursuit domain and argue that the poor performance that we observe is caused by frequent prototype collisions. We show that dynamic prototype allocation and adaptation can give better results by reducing these collisions. By using our novel approach about fuzzy Kanerva-based function approximation, that uses a fine-grained fuzzy membership grade to describe a state-action pair's adjacency with respect to each prototype, we give some results that prototype collisions are completely eliminated and learning performance is greatly improved. We further show that prototype density varies widely across the state-action space and that this variation causes prototypes' receptive fields to be unevenly distributed. This distribution limits the ability of fuzzy Kanerva Coding to achieve better results. It can be observed that fuzzy Kanerva Coding allows prototypes to adaptively tune their receptive fields for a target application. We conclude that fuzzy Kanerva Coding with prototype tuning and adaptation can significantly improve a reinforcement learner's ability to solve large-scale multi-agent problems. [ABSTRACT FROM AUTHOR]

In the paper, an application of dynamic programming approach to global optimization of approximate association rules relative to coverage and length is presented. It is an extension of the dynamic programming approach to optimization of decision rules to inconsistent tables. Experimental results with data sets from UCI Machine Learning Repository are included. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to find t-best approximation in a fuzzy normed space via the optimization formulation. It is formulated as a constrained minimization problem which is solved by using penalty method. Each penalty is solved by using inexact steepest descent algorithm. [ABSTRACT FROM AUTHOR]

The Orienteering Problem with Time Windows (OPTW) is an optimisation NP-hard problem. This paper proposes a hybrid genetic algorithm (GAPR) for approximating a solution to the OPTW. Instead of a crossover we use a path relinking (PR) strategy as a form of intensification solution. PR generates a new solution by exploring trajectories between two random solutions: genes not present in one solution are included in the other one. Experiments performed on popular benchmark instances show that the proposed GAPR gives good quality solutions using short computing times. Moreover, GAPR gives new best solutions for some test instances. [ABSTRACT FROM AUTHOR]

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification - exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage). [ABSTRACT FROM AUTHOR]

Globally optimal triangulations and pseudo-triangulations are difficult to be found by deterministic methods as, for most type of criteria, no polynomial algorithm is known. In this work, we consider the Minimum Weight Triangulation (MWT) and Minimum Weight Pseudo-Triangulation (MWPT) problems of a given set of n points in the plane. This paper shows how the Ant Colony Optimization (ACO) metaheuristic can be used to find high quality triangulations and pseudo-triangulations of minimum weight. For the experimental study presented here we have created a set of instances for MWT and MWPT problems since no reference to benchmarks for these problems were found in the literature. Through the experimental evaluation, we assess the applicability of the ACO metaheuristic for MWT and MWPT problems considering greedy and Simulated Annealing algorithms. [ABSTRACT FROM AUTHOR]

The paper studies stochastic optimization problems in Reproducing Kernel Hilbert Spaces (RKHS). The objective function of such problems is a mathematical expectation functional depending on decision rules (or strategies), i.e. on functions of observed random parameters. Feasible rules are restricted to belong to a RKHS. This kind of problems arises in on-line decision making and in statistical learning theory. We solve the problem by sample average approximation combined with Tihonov's regularization and establish sufficient conditions for uniform convergence of approximate solutions with probability one, jointly with a rule for downward adjustment of the regularization factor with increasing sample size. [ABSTRACT FROM AUTHOR]

Evolutionary Algorithm provides a framework that is largely applicable to particular problems including multiobjective optimization problems, basically for the ease of their implementation and their capability to perform efficient parallel search. Indeed, in some cases, expensive multiobjective optimization evaluations might be a challenge to restrict the number of explicit fitness evaluations in multiobjective evolutionary algorithms. Accordingly, this article presents a novel approach that tackles this problem so as to not only decrease the number of fitness evaluations but also to improve the performance. During evolution, our proposed approach selects fit individuals based on the knowledge acquired throughout the search, and performs explicit fitness evaluations on these individuals. Acomprehensive comparative analysis of a wide range of well-established test problems, selected from both traditional and state-of-the-art benchmarks, has been presented. Afterward, the effectiveness of the obtained results is compared with some of the state-of-the-art methods using two well-known metrics- i.e. Hypervolume and Inverted Generational Distance (IGD). The experiments of our implemented approach is performed to illustrate that our proposal seems to be promising and would prove more efficient than other approaches in terms of both the performance and the computational cost. [ABSTRACT FROM AUTHOR]

In many applications of kernel method, such as support vector machine (SVM), the performance greatly depends on the choice of kernel. It is not always clear what is the most suitable kernel for one specified task. Multiple kernel learning (MKL) allows to optimize over linear combinations of kernels. A two-step approach, which alternately optimizes the standard SVM and kernel weights, is proposed to solve the non-convex MKL problem. The generalized convexity of MKL is studied which guarantees the strong duality of the corresponding optimization problem. A further contribution is the utilization of a computationally efficient inexact-projection-based method to optimize the standard SVM. In addition, a nonmonotone gradient method is proposed to optimize the kernel weights and the Hessian matrix is approximated with a variation of Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) approach. The global convergence of the proposed two-step approach is analyzed. The utility of the proposed scheme is demonstrated by empirical study with several datasets, and its performance is compared with state-of-the-art methods in terms of accuracy and scalability. The results show that the proposed method performs as well as existing methods in accuracy, but takes much less time to converge to the stationary point. [ABSTRACT FROM AUTHOR]

To reduce the computation time needed in solving a practical optimization problem of electromagnetic devices, a fast and efficient global optimization method based on the combination of the Moving Least Squares (MLS) approximation and the Particle Swarm Optimization (PSO) method is proposed. Numerical examples are given to demonstrate the efficiency and robustness of the proposed method. [ABSTRACT FROM AUTHOR]