Extremal optimization vs. learning automata: Strategies for spin selection in portfolio selection problems

Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.     

Direct Adaptive Neurocontrol of Structures under Earth Vibration

A direct adaptive neurocontroller is proposed to reduce structure response to earth vibrations by actively creating an equal but opposite force to that of the first mode force of the structure. While earthquake forces are generally considered to be unpredictable, the short-term predictions by the proposed neurocontrol architecture significantly reduce structure vibrations. To demonstrate its general applicability and utility to future earthquakes, the proposed adaptation algorithm is also shown to be asymptotically convergent. The approach is validated by several simulations in which actual time series from the Hachino, Northridge, Kobe, and Bam earthquakes are applied against structures of various heights, three-, five-, and seven-story structures. The simulation results are then compared with those of a conventional linear quadratic regulator. Results indicate a significant and consistent improvement in minimal structure displacement.     

Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems

An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.     

Course timetabling using evolutionary operators

Timetabling is the problem of scheduling a set of events while satisfying various constraints. In this paper, we develop and study the performance of an evolutionary algorithm, designed to solve a specific variant of the timetabling problem. Our aim here is twofold: to develop a competitive algorithm, but more importantly, to investigate the applicability of evolutionary operators to timetabling. To this end, the introduced algorithm is tested using a benchmark set. Comparison with other algorithms shows that it achieves better results in some, but not all instances, signifying strong and weak points. To further the study, more comprehensive tests are performed in connection with another evolutionary algorithm that uses strictly group-based operators. Our analysis of the empirical results leads us to question single-level selection, proposing, in its place, a multi-level alternative.     

BRAIN EMOTIONAL LEARNING-BASED PATTERN RECOGNIZER

In this article, the brain emotional learning-based pattern recognizer (BELPR) is proposed to solve multiple input–multiple output classification and chaotic time series prediction problems. BELPR is based on an extended computational model of the human brain limbic system that consists of an emotional stimuli processor. The BELPR is model free and learns the patterns in a supervised manner and evaluates the output(s) using the activation function tansig. In the numerical studies, various comparisons are made between BELPR and a multilayer perceptron (MLP) with a back-propagation learning algorithm. The methods are tested to classify 12 UCI (University of California, Irvine) machine learning data sets and to predict activity indices of the Earth's magnetosphere. The main features of BELPR are higher accuracy, decreased time and spatial complexity, and faster training.     

Position,Jacobian and workspace analysis of a 3-PSP spatial parallel manipulator

This paper investigates the problems of kinematics, Jacobian, singularity and workspace analysis of a spatial type of 3-PSP parallel manipulator. First, structure and motion variables of the robot are addressed. Two operational modes, non-pure translational and coupled mixed-type are considered. Two inverse kinematics solutions, an analytical and a numerical, for the two operational modes are presented. The direct kinematics of the robot is also solved utilizing a new geometrical approach. It is shown, unlike most parallel robots, the direct kinematics problem of this robot has a unique solution. Next, analytical expressions for the velocity and acceleration relations are derived in invariant form. Auxiliary vectors are introduced to eliminate passive velocity and acceleration vectors. The three types of conventional singularities are analyzed. The notion of non-pure rotational and non-pure translational Jacobian matrices is introduced. The non-pure rotational and non-pure translational Jacobian matrices are combined to form the Jacobian of constraint matrix which is then used to obtain the constraint singularity. Finally, two methods, a discretization method and one based on direct kinematics are presented and robot non-pure translation and coupled mixed-type reachable workspaces are obtained. The influence of tool length on workspace is also studied.     

An adaptive ordered fuzzy time series with application to FOREX

An adaptive ordered fuzzy time series is proposed that employs an adaptive order selection algorithm for composing the rule structure and partitions the universe of discourse into unequal intervals based on a fast self-organising strategy. The automatic order selection of FTS as well as the adaptive partitioning of each interval in the universe of discourse is shown to greatly affect forecasting accuracy. This strategy is then applied to prediction of FOREX market. Financial markets, such as FOREX, are generally attractive applications of FTS due to their poorly understood model as well as their great deal of uncertainty in terms of quote fluctuations and the behaviours of the humans in the loop. Specifically, since the FOREX market can exhibit different behaviours at different times, the adaptive order selection is executed online to find the best order of the FTS for current prediction. The order selection module uses voting, statistical analytic and emotional decision making agents. Comparison of the proposed method with earlier studies demonstrates improved prediction accuracy at similar computation cost.     

V-Lab-a virtual laboratory for autonomous agents-SLA-based learning controllers

In this paper, we present the use of stochastic learning automata (SLA) in multiagent robotics. In order to fully utilize and implement learning control algorithms in the control of multiagent robotics, an environment for simulation has to be first created. A virtual laboratory for simulation of autonomous agents, called V-Lab is described. The V-Lab architecture can incorporate various models of the environment as well as the agent being trained. A case study to demonstrate the use of SLA is presented.     

A novel constructive-optimizer neural network for the traveling salesman problem.

In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.