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• A novel SMO inspired by mimicking behavior to solve optimization problems is presented. • SMO includes a mimic operator to simulate search in the response space. • SMO does not require control operator respect to other Meta heuristic methods. • SMO solve optimization problems with minimum population size. • SMO is compared with 14 well-known and state of the art optimization algorithms. Increase in complexity of real world problems has provided an area to explore efficient methods to solve computer science problems. Meta-heuristic methods based on evolutionary computations and swarm intelligence are instances of techniques inspired by nature. This paper presents a novel social mimic optimization (SMO) algorithm inspired by mimicking behavior to solve optimization problems. The proposed algorithm is evaluated using 23 test functions. Obtained results are compared with 14 known optimization algorithms including Whale optimization algorithm (WOA), Grasshopper optimization algorithm (GOA), Particle Swarm Optimization (PSO), Stochastic fractal search (SFS), Grey Wolf Optimizer (GWO), Optics Inspired Optimization (OIO), League Championship Algorithm (LCA), Wind Driven Optimization (WDO), Harmony search (HS), Firefly Algorithm (FA), Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), Bat Algorithm (BA), and Teaching Learning Based Optimization (TLBO). Obtained results indicate higher capability of the SMO algorithm in solving high-dimensional decision variables. Furthermore, SMO is used to solve two classic engineering design problems. Three important features of SMO are simple implementation, solving optimization problems with minimum population size and not requiring control parameters. Results of various evaluations show superiority of the proposed method in finding the optimal solution with minimum function evaluations. This superiority is achieved based on reducing number of initial population. The proposed method can be applied to applications like automatic evolution of robotics, automatic control of machines and innovation of machines in finding better solutions with less cost. [ABSTRACT FROM AUTHOR]

Abstract: The utilization of populations is one of the most important features of evolutionary algorithms (EAs). There have been many studies analyzing the impact of different population sizes on the performance of EAs. However, most of such studies are based on computational experiments, except for a few cases. The common wisdom so far appears to be that a large population would increase the population diversity and thus help an EA. Indeed, increasing the population size has been a commonly used strategy in tuning an EA when it did not perform as well as expected for a given problem. He and Yao (2002)  showed theoretically that for some problem instance classes, a population can help to reduce the runtime of an EA from exponential to polynomial time. This paper analyzes the role of population further in EAs and shows rigorously that large populations may not always be useful. Conditions, under which large populations can be harmful, are discussed in this paper. Although the theoretical analysis was carried out on one multimodal problem using a specific type of EAs, it has much wider implications. The analysis has revealed certain problem characteristics, which can be either the problem considered here or other problems, that lead to the disadvantages of large population sizes. The analytical approach developed in this paper can also be applied to analyzing EAs on other problems. [Copyright &y& Elsevier]

Abstract: In an earlier paper [Fukushima, K., & Tohyama, K. (2005). Analysis of optic flow: a neural network model. In ICONIP 2005, International Conference on Neural Information Processing (pp. 312–317); Tohyama, K., & Fukushima, K. (2005). Neural network model for extracting 98 optic flow. Neural Networks, 18(5–6), 549–556], we proposed a neural network extracting optic flow based on vector field hypothesis. We started discussion there, however, from the stage where local velocities of visual objects have already been extracted. This paper proposes a new mechanism of extracting local velocity from retinal images, and adds it to the previous network. The network has a hierarchical multilayered architecture. X- and Y-cells of the retina extract spatial and temporal contrast of brightness. The network contains several types of V1 cells, namely, S- C- and V-cells. S- and C-cells extract orientated edges. V-cells extract local velocities, based on the signals from Y- and S-cells. MT cells extract relative velocities between adjoining small visual fields. MST cells add the responses of MT cells to extract a specific optic flow, such as rotation and expansion/contraction. The difference in type of optic flow extracted by MST cells can be created simply by the difference in relative locations of inhibitory to excitatory areas in the receptive fields of MT cells. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a co-evolutionary immune algorithm for the multi-criteria decision making (MCDM) model, and use the model to solve the large scale garment matching problem. Size fitting problem is a main obstacle to large scale garment sales and online sales because it is difficult to find the fit garments by the general size information. This study regards the fit garment matching problem as a MCDM model with the constraints of size satisfaction. An immune co-evolutionary algorithm is used to search the fit garments from the candidate garments in the stock. The garments in the stock are taken as antibodies and the customer request as antigen. The concepts of ideal garment and loose garment are virtual garment to model the customer request. Two affinity measures including dominance affinity and distance affinity are defined to represent the similarity of antibody to antibody and that of antibody to antigen. Correspondingly, the fit garments are chosen by two methods: the Pareto optimal garments by the MCDM solving algorithm, and the optimal garments with the minimal distance affinity to the ideal garments. An evaluation model on garments is proposed to evaluate the fit garments by the affinity measures. Based on the experiment data from the effective study of detail factors of female trousers, the proposed model and algorithm demonstrate to be a feasible and effective attempt aiming at a valuable problem and provide the key tool for garment store sale system or online garment order system to support accurate garment size matching. [Copyright &y& Elsevier]

Abstract: A weighted switching strategy and an inner-loop compensator are presented in this paper to design an observer-based tracker for a decentralized closed-loop cascaded system with a saturating actuator and state constraints. The LQR design methodology for the observer-based tracker is proposed to simplify the complexity of the decentralized control. The realizable sample-data controller with a low-gain property and a high design performance is realized through the digital redesign method. For obtaining a better design performance, evolutionary programming is then presented to tune the parameters of the tracker. Some examples are also presented to demonstrate the effectiveness of the proposed methodology. [Copyright &y& Elsevier]

Abstract: The creation and the evaluation of aesthetic artifacts are tasks related to design, music and art, which are highly interesting from the computational point of view. Nowadays, Artificial Intelligence systems face the challenge of performing tasks that are typically human, highly subjective, and eventually social. The present paper introduces an architecture which is capable of evaluating aesthetic characteristics of artifacts and of creating artifacts that obey certain aesthetic properties. The development methodology and motivation, as well as the results achieved by the various components of the architecture, are described. The potential contributions of this type of systems in the context of digital art are also considered. [Copyright &y& Elsevier]

Abstract: Many man-made objects are composed of a number of some simple geometric shapes such as lines, circles, rectangles, etc. Therefore, the detection of rectangular objects is an important issue to some practical applications such as the detection of buildings and vehicles in aerial imagery, the detection of license plates in car images, etc. Several methods have been proposed for solving the problem of the detection of rectangular objects. While some approaches are based on the detecting lines, some approaches are based on the Hough transform. Each approach has its own advantages and disadvantages (e.g., computational load). In this paper, we propose a class of neural networks with a special type of neural junctions for the detection of rectangular objects. The proposed neural networks can be trained in either an unsupervised mode or a batch mode. In contrast to some popular clustering algorithms such as the fuzzy -means algorithm and the fuzzy -rectangular shells algorithm, our approach is not based on minimizing an objective function but based on the idea of competitive learning. Based on the idea of competitive learning, the computational load can be decreased. Several data sets were tested to illustrate the effectiveness of our proposed approach. [Copyright &y& Elsevier]

Abstract: Automatic birdsong recognition using prediction-based singleton-type recurrent neural fuzzy networks (SRNFNs) is proposed in this paper. The recognition task consists of two stages. The first stage segments a significant portion from a birdsong sequence and the second stage performs recognition. For birdsong segmentation, an easy but effective segmentation approach based on time domain energy is proposed. For recognition, the linear predictive coding (LPC) coefficients of each frame in a segmented birdsong are extracted and used as features. These features are fed as inputs to SRNFN recognizers. The SRNFN is constructed by recurrent fuzzy if–then rules with fuzzy singletons in the consequences, and its recurrent aspect makes it suitable for processing patterns with temporal characteristics. In birdsong recognition, the sample prediction technique is used, where one SRNFN is responsible for learning the temporal birdsong relationships of only one species. The prediction error of each SRNFN is then used as a criterion for recognition. Experiments with 10 species of birds and their songs are performed, and a high recognition rate is achieved. Comparisons with a Takagi–Sugeno–Kang (TSK)-type recurrent fuzzy network (TRFN) and backpropagation neural network are also made in the experiments. [Copyright &y& Elsevier]

Abstract: Several implementations of Feedforward Neural Networks have been reported in scientific papers. These implementations do not allow the direct use of off-line trained networks. Usually, the problem is the lower precision (compared to the software used for training) or modifications in the activation function. In the present work, a hardware solution called Artificial Neural Network Processor, using a FPGA, fits the requirements for a direct implementation of Feedforward Neural Networks, because of the high precision and accurate activation function that were obtained. The resulting hardware solution is tested with data from a real system to confirm that it can correctly implement the models prepared off-line with MATLAB. [Copyright &y& Elsevier]

Abstract: This article presents a series of hardware implementations of a general regression neural network (GRNN) using FPGAs. The paper describes the study of this neural network using different fixed and floating point implementations. The implementation includes training as well as testing of the network. It is focused on precision loss and area and speed results of the resulting neural network coprocessor that can be used in a System on Programmable Chip. A GRNN is able to approximate functions and it has been used in control, prediction, fault diagnosis, engine management among others. They are mainly implemented as software entities because they require a great amount of complex mathematical operations. With the increasing power and capabilities of current FPGAs, now it is possible not only to translate them into hardware but, due to the reconfigurable feature of these devices, to explore different hardware/software partitions as well. These hardware implementations increase both the speed and performance of these neural networks and the designer can select the area–speed trade-off that best fits the application. [Copyright &y& Elsevier]

Abstract: In this paper, a model is considered to describe the dynamics of a class of non-autonomous neural networks with time-varying delays. By applying the properties of M-matrix, the techniques of inequality analysis and the Banach fixed point theorem, we obtain a series of new criteria on the dissipativity and existence of periodic attractor. Our results can extend and improve earlier ones. [Copyright &y& Elsevier]

The application of artificial neural networks (ANNs) to solve wind engineering problems has received increasing interests in recent years. This paper is concerned with developing two ANN approaches (a backpropagation neural network [BPNN] and a fuzzy neural network [FNN]) for the prediction of mean, root-mean-square (rms) pressure coefficients and time series of wind-induced pressures on a large gymnasium roof. In this study, simultaneous pressure measurements are made on a large gymnasium roof model in a boundary layer wind tunnel and parts of the model test data are used as the training sets for developing two ANN models to recognize the input–output patterns. Comparisons of the prediction results by the two ANN approaches and those from the wind tunnel test are made to examine the performance of the two ANN models, which demonstrates that the two ANN approaches can successfully predict the pressures on the entire surfaces of the large roof on the basis of wind tunnel pressure measurements from a certain number of pressure taps. Moreover, the FNN approach is found to be superior to the BPNN approach. It is shown through this study that the developed ANN approaches can be served as an effective tool for the design and analysis of wind effects on large roof structures. [Copyright &y& Elsevier]

Abstract: This paper proposes a new algorithm for a class of binary images thinning by using two PCNNs (pulse coupled neural networks). Once the travelling pulses of the two PCNNs meet, the thinning result is obtained. The criterion of pulses meeting is given, and the parameters of the PCNNs are also specified, which make the implementation of the proposed thinning algorithm easier. The algorithm is used to thin such a class of binary images, which separate the original images into two regions, as circularity-like images and ribbon-like shapes. Experimental results show that the proposed algorithm is efficient in extracting the skeleton of images (such as circularity-like images, ribbon-like shapes, handwriting ‘0’, etc.). [Copyright &y& Elsevier]

This paper presents an algorithm for solving security constrained optimal power flow problem through the application of evolutionary programming (EP). The controllable system quantities in the base-case state are optimised to minimize some defined objective function subject to the base-case operating constraints as well as the contingency-case security constraints. An IEEE 30-bus system is taken for investigation. The security constrained optimal power flow results obtained using EP are compared with those obtained using conventional security constrained optimal power flow. The investigations reveal that the proposed algorithm is relatively simple, reliable and efficient and suitable for on-line applications. [Copyright &y& Elsevier]

This paper presents architectures for comparator trees capable of finding the minimum value of a large number of inputs. Such circuits are of general applicability although the intended application for which the circuits were designed is the winner-take-all function of a digital implementation of a neural network based on the self organising map. Mechanisms for reducing delay based on look-ahead logic within individual comparators and mechanisms based on multiplexor architectures of a comparator are compared for both propagation delay and area. [Copyright &y& Elsevier]