Use of probabilistic neural networks to solve classification problems in surgery

The paper considers the classification of peritonitis-stricken patients with regard to the outcome of the operation and the probable result of the treatment. A probabilistic neural network is offered as a classifier.     

A constructive algorithm for binary neural networks: the oil-spot algorithm.

This paper presents a constructive training algorithm for supervised neural networks. The algorithm relies on a topological approach, based on the representation of the mapping of interest onto the binary hypercube of the input space. It dynamically constructs a two-layer neural network by involving successively binary examples. A convenient treatment of real-valued data is possible by means of a suitable real-to-binary codification. In the case of target functions that have efficient halfspace union representations, simulations show the constructed networks result optimized in terms of number of neurons.     

A constructive algorithm to synthesize arbitrarily connected feedforward neural networks

In this work we present a constructive algorithm capable of producing arbitrarily connected feedforward neural network architectures for classification problems. Architecture and synaptic weights of the neural network should be defined by the learning procedure. The main purpose is to obtain a parsimonious neural network, in the form of a hybrid and dedicate linear/nonlinear classification model, which can guide to high levels of performance in terms of generalization. Though not being a global optimization algorithm, nor a population-based metaheuristics, the constructive approach has mechanisms to avoid premature convergence, by mixing growing and pruning processes, and also by implementing a relaxation strategy for the learning error. The synaptic weights of the neural networks produced by the constructive mechanism are adjusted by a quasi-Newton method, and the decision to grow or prune the current network is based on a mutual information criterion. A set of benchmark experiments, including artificial and real datasets, indicates that the new proposal presents a favorable performance when compared with alternative approaches in the literature, such as traditional MLP, mixture of heterogeneous experts, cascade correlation networks and an evolutionary programming system, in terms of both classification accuracy and parsimony of the obtained classifier.     

A differential evolution algorithm to solve redundancy allocation problems

To improve system reliability without changing its nature, three methods are proposed. The first method uses more reliable components and the second method provides redundant components within the system. The third method is a combination of these two methods. The redundancy allocation problem (RAP) finds the appropriate mix of components and redundancies within a system to maximize its reliability or minimize its cost due to several constraints, such as cost, weight, and volume. This paper presents a methodology to solve the RAP, which is an NP‐hard problem, modeled with discrete variables. In this paper, we use a metaheuristic to solve the RAP of a series–parallel system with a mix of components. Our metaheuristic offers a practical method with specific solution encoding, and combines a penalty function to solve large instances of the relaxed RAP, where different types of components can be used in parallel. The efficiency of the algorithm was tested through a set of well‐known benchmark problems from the literature. Testing of the algorithm achieved satisfactory results in reasonable computing time.     

用启发算法和神经网络法解决二维不规则零件排样问题

本文提出一种用启发算法和神经网络法相结合的算法解决二维不规则零件的排料问题。此算法具有优化效果好、自动化程度高、并且速度快等特点。     

A constructive design method for two-layer perceptrons and its applicatio to the design of modular neural networks

Abstract: A multilayer perceptron is known to be capable of approximating any smooth function to any desired accuracy if it has a sufficient number of hidden neurons. But its training, based on the gradient method, is usually a time consuming procedure that may converge toward a local minimum, and furthermore its performance is greatly influenced by the number of hidden neurons and their initial weights. Usually these crucial parameters are determined based on the trial and error procedure, requiring much experience on the designer's part.
In this paper, a constructive design method (CDM) has been proposed for a two-layer perceptron that can approximate a class of smooth functions whose feature vector classes are linearly separable. Based on the analysis of a given data set sampled from the target function, feature vectors that can characterize the function'well'are extracted and used to determine the number of hidden neurons and the initial weights of the network. But when the classes of the feature vectors are not linearly separable, the network may not be trained easily, mainly due to the interference among the hyperplanes generated by hidden neurons. Next, to compensate for this interference, a refined version of the modular neural network (MNN) has been proposed where each network module is created by CDM. After the input space has been partitioned into many local regions, a two-layer perceptron constructed by CDM is assigned to each local region. By doing this, the feature vector classes are more likely to become linearly separable in each local region and as a result, the function may be approximated with greatly improved accuracy by MNN. An example simulation illustrates the improvements in learning speed using a smaller number of neurons.     

Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems.

This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic algorithm based on tournament selection is proposed. Also, HCOEA has adopted a parallel local search operator that implements a clustering partition of the population and multiparent crossover to generate the offspring population. Then, nondominated individuals in the offspring population are used to replace the dominated individuals in the parent population. Meanwhile, the best infeasible individual replacement scheme is devised for the purpose of rapidly guiding the population toward the feasible region of the search space. During the evolutionary process, the global search model effectively promotes high population diversity, and the local search model remarkably accelerates the convergence speed. HCOEA is tested on 13 well-known benchmark functions, and the experimental results suggest that it is more robust and efficient than other state-of-the-art algorithms from the literature in terms of the selected performance metrics, such as the best, median, mean, and worst objective function values and the standard deviations.     

A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm

Genetic algorithms (GAs), which are directed stochastic hill climbing algorithms, are a commonly used optimization technique and are generally applied to single criterion optimization problems with fairly complex solution landscapes. There has been some attempts to apply GA to multicriteria optimization problems. The GA selection mechanism is typically dependent on a single-valued objective function and so no general methods to solve multicriteria optimization problems have been developed so far. In this paper, a new method of transformation of the multiple criteria problem into a single-criterion problem is presented. The problem of transformation brings about the need for the introduction of thePareto set estimation method to perform the multicriteria optimization using GAs. From a given solution set, which is the population of a certain generation of the GA, the Pareto set is found. The fitness of population members in the next GA generation is calculated by a distance metric with a reference to the Pareto set of the previous generation. As we are unable to combine the objectives in some way, we resort to this distance metric in the positive Pareto space of the previous solutions, as the fitness of the current solutions. This new GA-based multicriteria optimization method is proposed here, and it is capable of handling any generally formulated multicriteria optimization problem. The main idea of the method is described in detail in this paper along with a detailed numerical example. Preliminary computer generated results show that our approach produces better, and far more Pareto solutions, than plain stochastic optimization methods.     

Parallel partitioning method (PPM): A new exact method to solve bi-objective problems

In this paper, we propose a new exact method, called the parallel partitioning method (PPM), able to solve efficiently bi-objective problems. This method is based on the splitting of the search space into several areas leading to elementary exact searches. We compare this method with the well-known two-phase method (TPM). Experiments are carried out on a bi-objective permutation flowshop problem (BOFSP). During experiments the proposed PPM is compared with two versions of TPM: the basic TPM and an improved TPM dedicated to scheduling problems. Experiments show the efficiency of the new proposed method.     

Modified cuckoo optimization algorithm (MCOA) to solve graph coloring problem

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature, such as particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo optimization algorithm (COA). Recently introduced COA, has proven its excellent capabilities, such as faster convergence and better global minimum achievement. In this paper a new approach for solving graph coloring problem based on COA was presented. Since COA at first was presented for solving continuous optimization problems, in this paper we use the COA for the graph coloring problem, we need a discrete COA. Hence, to apply COA to discrete search space, the standard arithmetic operators such as addition, subtraction and multiplication existent in COA migration operator based on the distance's theory needs to be redefined in the discrete space. Redefinition of the concept of the difference between the two habitats as the list of differential movements, COA is equipped with a means of solving the discrete nature of the non-permutation. A set of graph coloring benchmark problems are solved and its performance is compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method.     

A new result for projection neural networks to solve linear variational inequalities and related optimization problems

In recent years, a projection neural network was proposed for solving linear variational inequality (LVI) problems and related optimization problems, which required the monotonicity of LVI to guarantee its convergence to the optimal solution. In this paper, we present a new result on the global exponential convergence of the projection neural network. Unlike existing convergence results for the projection neural network, our main result does not assume the monotonicity of LVI problems. Therefore, the projection neural network can be further guaranteed to solve a class of non-monotone LVI and non-convex optimization problems. Numerical examples illustrate the effectiveness of the obtained result.     

Convex Subspace Routing (CSR): Routing via anchor-based convex virtual subspaces in sensor networks

Convex Subspace Routing (CSR) is a novel approach for routing in sensor networks using anchor-based virtual coordinates. Unlike geographical routing schemes that require physical location information of nodes, obtaining which is often difficult, error-prone and costly, the Virtual Coordinate (VC) based schemes simply characterize each node by a vector of shortest hop distances to a selected subset of nodes known as anchors. Even though VC based routing (VCR) schemes benefits from having connectivity information implicitly embedded within the VCs, VCs lack the directional information available with physical coordinates. The major issues affecting routing using VCs are addressed. Due to local minima problem in the virtual space, the VCR schemes rely on backtracking or hill climbing techniques to overcome the local minima. Convex Subspace Routing, in contrast, avoids using anchors that cause local minima. It dynamically selects subsets of anchors that define subspaces to provide convex distance functions from source to destination. Consequently, it is less sensitive to anchor placement and over anchoring, and does not require tracking route history for backtracking, resulting in shorter packet lengths and energy efficient operation. Three techniques for selection of convex subspaces are proposed and evaluated. Performance evaluation for several different network topologies indicates that CSR significantly outperforms the existing VCR scheme, Logical Coordinate Routing (LCR), while being competitive with geographic coordinate based Greedy Perimeter Stateless Routing (GPSR), even though latter makes use of node location information.     

求解VRP问题的混合鱼群遗传优化算法

首先对物流配送中的一般车辆优化调度问题（VRP）进行了分析,并为之建立了相应的数学模型。随后设计了一个人工混合鱼群算法,并研究了如何应用该算法解决车辆优化调度问题,该算法在初期阶段应用人工鱼群算法迅速获得阶段最优解,在后期阶段应用遗传算法寻求最优解。最后通过仿真实验验证了该算法具有求解速度快,性能稳定等优点。     

A meta-heuristic algorithm to solve quadratic assignment formulations of cell formation problems without presetting number of cells

The purpose of cellular manufacturing (CM) is to find part-families and machine cells which form self-sufficient units of production with a certain amount of autonomy that result in easier control (Kusiak, 1987, 1990). One of the most important steps in CM is to optimally identify cells from a given part-machine incidence matrix. Several formulations of various complexities are proposed in the literature to deal with this problem. One of the mostly known formulations for CM is the quadratic assignment formulation (Kusiak and Chow, 1988). The problem with the quadratic assignment based formulation is the difficulty of its solution due to its combinatorial nature. The formulation is also known as NP-hard (Kusiak and Chow, 1988). In this paper a novel simulated annealing based meta-heuristic algorithm is developed to solve quadratic assignment formulations of the manufacturing cell formation problems. In the paper a novel solution representation scheme is developed. Using the proposed solution representation scheme, feasible neighborhoods can be generated easily. Moreover, the proposed algorithm has the ability to self determine the optimal number of cell during the search process. A test problem is solved to present working of the proposed algorithm.     

Fast implementation of partial Gram-Schmidt algorithm using tree data structure with sequential row pointers data structure to solve structural problems (PGS)

In this paper, tree data structure with sequential row pointers data structure is developed and used to implement the Partial Gram-Schmidt triangularization algorithm. This algorithm is used to find the solution of linear equations arising from many structural and network problems where the original matrix is non-symmetric and highly sparse, that is, the ratio between zeros and nonzeros elements is very large. An algorithm is developed using the above data structure and results are compared with the method given in M.A. Ajiz, Incomplete Methods For Computer Structural Analysis, PhD Thesis, Queen's University Belfast, 1982 and Ajiz & Jennings, Int. J. Num. Meth. Engng., 20, 949–966, 1984. Conjugate gradients algorithm is used in conjunction with Partial Gram-Schmidt algorithm to obtain the solution of problems obtaining four figure accuracy.     

A randomized algorithm for controlling the round-off error accumulation in recursive digital frequency synthesis (DFS)

Recursive methods for digital frequency synthesis (DFS) exhibit numerical instability due to the round-off error propagation and accumulation in the finite precision digital computations. Because of this, despite their simplicity and other desirable features, recursive DFS methods are generally avoided in practice. In this paper we present a method that solves this numerical instability problem for the coupled form oscillator (CFO) method in a simple manner by randomizing the selection of the rotation phase-step. The internal process uncertainty, induced by the truncation of intermediate computation results, is used for randomization. Interestingly this results in the self-stabilizing property of the proposed method.     

A precise and stable machine learning algorithm: eigenvalue classification (EigenClass)

Neural Computing and Applications - In this study, a precise and efficient eigenvalue-based machine learning algorithm, particularly denoted as Eigenvalue Classification (EigenClass) algorithm, has...     

A firefly algorithm for power management in wireless sensor networks (WSNs)

The Journal of Supercomputing - In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize...     

Applying the MOVNS (multi-objective variable neighborhood search) algorithm to solve the path planning problem in mobile robotics

Mobile robots must calculate the appropriate navigation path before starting to move to its destination. This calculation is known as the Path Planning (PP) problem. The PP problem is one of the most researched topics in mobile robotics. Taking into account that the PP problem is an NP-hard problem, Multi-Objective Evolutionary Algorithms (MOEAs) are good candidates to solve this problem. In this work, a new multi-objective evolutionary approach based on the Variable Neighborhood Search (MOVNS) is proposed to solve the PP problem. To the best of our knowledge, this is the first time that MOVNS is proposed to solve the path planning of mobile robots. The proposed MOVNS handles three different objectives in order to obtain accurate and efficient paths. These objectives are: the path safety, the path length, and the path smoothness (related to the energy consumption). Furthermore, in order to test the proposed MOEA, we have used eight realistic scenarios for the paths calculation. On the other hand, we also compared our proposal with other approaches of the state of the art, showing the advantages of MOVNS. In particular, in order to evaluate the obtained results we applied different quality metrics. Moreover, to demonstrate the statistical robustness of the obtained results we also performed a statistical analysis. Finally, the study shows that the proposed MOVNS is a good alternative to solve the PP problem, producing good paths with less length, more safety, and more smooth movements. We think this is an important contribution to the mobile robotics, and therefore, to the field of expert and intelligent systems.