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The application mapping problem is an NP-hard combinatorial optimization problem in network-on-chip (NoC) design. Applications of size ($n$ $ >$ 30) cannot be solved optimally by an exact algorithm in reasonable time, and the evolutionary algorithms have drawn the attention of NoC researchers. In this paper, we propose a new effective optimization method based on the discrete particle swarm optimization framework, which includes the novel principles for representation, velocity computing, and position-updating of the particles. In our proposed method, particles are allowed to swing between elite and regular pools, and a simple local search procedure is applied on elite particles to exploit the promising solutions. Extensive computational studies using standard benchmark instances and task graphs for free (TGFF) random instances reveal that the proposed optimization algorithm is able to attain the best results, and thus competes very favorably with the previously proposed heuristic approaches. A stability analysis and the two-sided Wilcoxon rank sum tests are also presented to shed light on the robust behavior of the algorithm. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an appropriate combinatorial optimization technique for solving the optimal sizing problem of hybrid ac–dc microgrids. A novel two-stage iterative approach is proposed. In the first stage, a meta-heuristic technique based on a tailor-made genetic algorithm is used to tackle the optimal sizing problem, while, in the second, a non-linear solver is deployed to solve the operational problem subject to the obtained design/investment decisions. The proposed approach, being able to capture technical characteristics such as voltage and frequency through a detailed power flow algorithm, provides accurate solutions and, therefore, can address operational challenges of microgrids. Its capability to additionally capture contingencies ensures that the proposed sizing solutions are suitable both during normal operation and transient states. Finally, the genetic algorithm provides convergence of the model with relative computational simplicity. The proposed model is applied to a generalizable microgrid comprising of ac and dc generators and loads, as well as various types of storage technologies in order to demonstrate the benefits. The load and natural resources data correspond to real data. [ABSTRACT FROM AUTHOR]

In this paper, we apply an immune-inspired approach to design ensembles of heterogeneous neural networks for classification problems. Our proposal, called Bayesian artificial immune system, is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Among the additional attributes provided by the Bayesian framework inserted into an immune-inspired search algorithm are the automatic control of the population size along the search and the inherent ability to promote and preserve diversity among the candidate solutions. Both are attributes generally absent from alternative estimation of distribution algorithms, and both were shown to be useful attributes when implementing the generation and selection of components of the ensemble, thus leading to high-performance classifiers. Several aspects of the design are illustrated in practical applications, including a comparative analysis with other attempts to synthesize ensembles. [ABSTRACT FROM AUTHOR]

Extracting information from multitrial magnetoen-cephalography or electroencephalography (EEG) recordings is challenging because of the very low SNR, and because of the inherent variability of brain responses. The problem of low SNR is commonly tackled by averaging multiple repetitions of the recordings, also called trials, but the variability of response across trials leads to biased results and limits interpretability. This paper proposes to decode the variability of neural responses by making use of graph representations. Our approach has several advantages compared to other existing methods that process single-trial data: first, it avoids the a priori definition of a model for the waveform of the neural response; second, it does not make use of the average data for parameter estimation; third, it does not suffer from initialization problems by providing solutions that are global optimum of cost functions; and last, it is fast. We proceed in two steps. First, a manifold learning algorithm, based on a graph Laplacian, offers an efficient way of ordering trials with respect to the response variability, under the condition that this variability itself depends on a single parameter. Second, the estimation of the variability is formulated as a combinatorial optimization that can be solved very efficiently using graph cuts. Details and validation of this second step are provided for latency estimation. Performance and robustness experiments are conducted on synthetic data, and results are presented on EEG data from a P300 oddball experiment. [ABSTRACT FROM AUTHOR]

Condition Based Maintenance (CBM) aims to balance two extreme sides (i.e., Corrective Maintenance (CM), and Preventive Maintenance (PM)) by observing and forecasting the real time health of machines. Recent developments in CBM revealed promising technologies for advanced fault detection, and forecasting. Traditional maintenance scheduling in CBM is based on the threshold setting on forecasted failure probability, or remaining useful life (RUL) for individual components. However, this approach may not give the best result for the system, because individual components are inter-related, and mutually dependent. It is not uncommon in systems that turning off a machine due to failure or maintenance causes other machinery or components to be turned off. Designing a comprehensive tool that optimizes availability & cost of the whole system incorporating prognostics information is crucial to fully benefit from CBM. The goal of this paper is to emphasize this need by demonstrating scenarios in CM, PM, and CBM; and to present a solution that optimizes system availability, and cost with system-maintenance constraints using genetic algorithms. The proposed tool acquires the forecasted failure probability of individual components from the prognostics module, and their reliability expectations after maintenance. The tradeoff between maintenance & failure is quantified in risk as the objective function to be minimized. The risk is minimized utilizing genetic algorithms for the whole system rather than individual components. The results of the proposed tool are compared with PM, CM, and CBM in which prognostics information of components are analyzed individually. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive penalty function for solving constrained optimization problems using genetic algorithms. The proposed method aims to exploit infeasible individuals with low objective value and low constraint violation. The number of feasible individuals in the population is used to guide the search process either toward finding more feasible individuals or searching for the optimum solution. The proposed method is simple to implement and does not need any parameter tuning. The performance of the algorithm is tested on 22 benchmark functions in the literature. The results show that the proposed approach is able to find very good solutions comparable to the chosen state-of-the-art designs. Furthermore, it is able to find feasible solutions in every run for all of the benchmark functions tested. [ABSTRACT FROM AUTHOR]

In this paper, we face the problem of designing accurate decision-making modules in measurement systems that need to be implemented on resource-constrained platforms. We propose a methodology based on multiobjective optimization and genetic algorithms (GAs) for the analysis of support vector machine (SVM) solutions in the classification error-complexity space. Specific criteria for the choice of optimal SVM classifiers and experimental results on both real and synthetic data will also be discussed. [ABSTRACT FROM AUTHOR]

In this paper, a new method is presented for a subpixelic land cover classification using both high-resolution structural information and coarse-resolution (CR) temporal information. To that aim, the linear mixture model is used for pixel disaggregation. It enables us to describe a CR time series in terms of the mixture of classes that are represented within each pixel. Then, the Bayes' rule and the maximum a posteriori criterion lead to the definition of an energy function whose minimum corresponds to the researched optimal classification. A theoretical analysis of the labeling errors that may be obtained using this energy function is provided, raising the main parameters for labeling performance. The optimal classification is computed by combining linear regressions and simulated annealing, leading to an unsupervised algorithm. The method is validated with numerical results obtained on two different agricultural scenes (i.e., the Danubian plain and the Coet Dan watershed). [ABSTRACT FROM AUTHOR]

In this paper, the authors have developed a novel algorithm for selective harmonic elimination in a pulsewidth-modulated inverter based on the foraging behavior of a colony of ants. The problem of selective harmonic elimination, together with output voltage regulation, is redrafted as an optimization problem, and the solution is sought through the novel concept. The proposed scheme has several advantages, such as computational simplicity, derivative-free operation, and guaranteed near-optimal convergence with random initial guess. It is shown that a newly introduced parameter in the algorithm—quantum of the step movement of ants—largely influences the speed of convergence of the algorithm. Theoretical and practical results are included. Furthermore, the proposed algorithm is compared with the conventional Newton-Raphson method and genetic algorithm, and conclusions are presented. [ABSTRACT FROM AUTHOR]

This paper presents a generalized model of damage caused to a complex multi-state series-parallel system by intentional attack. The model takes into account the defense strategy that presumes separation and protection of system elements. The defense strategy optimization methodology is suggested, based on the assumption that the attacker tries to maximize the expected damage of an attack. An optimization algorithm is presented that uses a universal generating function technique for evaluating the losses caused by system performance reduction, and a genetic algorithm for determining the optimal defense strategy. Illustrative examples of defense strategy optimization are presented. [ABSTRACT FROM AUTHOR]

The meta-heuristic methods, genetic algorithms (GAs), are frequently used to obtain optimal solutions for some complicated problems. However, due to the characteristic of natural evolution, the methods slowly converge the derived solutions to an optimal solution and are usually used to solve complicated and offline problems. While, in a real-world scenario, there are some complicated but real-time problems that require being solved within a short response time and have to obtain an optimal or near optimal solution due to performance considerations. Thus, the convergence speed of GAs becomes an important issue when it is applied to solve time-critical optimization problems. To address this, this paper presents a novel method, named hyper-generation GA (HG-GA), to improve the convergence speed of GAs. The proposed HG-GA breaks the general rule of generation-based evolution and uses a pipeline operation to accelerate the convergence speed of obtaining an optimal solution. Based on an example of a time-critical scheduling process in an optical network, both analysis and simulation results show that the HG-GA can generate more and better chromosomes than general GAs within the same evolutionary period. The rapid convergence property of the HG-GA increases its potential to solve many complicated problems in real-time systems. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid approach for the design of adaptive fuzzy controllers (FCs) in which two learning algorithms with different characteristics are merged together to obtain an improved method. The approach combines a genetic algorithm (GA), devised to optimize all the configuration parameters of the FC, including the number of membership functions and rules, and a Lyapunov-based adaptation law performing a local tuning of the output singletons of the controller, and guaranteeing the stability of each new controller investigated by the GA. The effectiveness of the proposed method is confirmed using both numerical simulations on a known case study and experiments on a nonlinear hardware benchmark. [ABSTRACT FROM AUTHOR]

A hybrid Taguchi genetic algorithm (HTGA) is applied in this paper to solve the problem of designing optimal digital infinite-impulse response (IIR) filters. The HTGA approach is a method of combining the traditional GA (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Based on minimizing the Lp-norm approximation error and minimizing the ripple magnitudes of both passband and stopband, a multicriterion combination is employed as the design criterion to obtain the optimal IIR filter that can fit different performance requirements. The proposed HTGA approach is effectively applied to solve the multiparameter and multicriterion optimization problems of designing the digital low-pass (LP), high-pass (HP), bandpass (BP), and bandstop (BS) filters. In these studied problems, there are many parameters and numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the proposed HTGA approach can obtain better digital IIR filters than the existing GA-based method reported recently in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is applied to solve the problem of tuning both network structure and parameters of a feedforward neural network. The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently enhance the genetic algorithms. Therefore, the HTGA approach can be more robust, statistically sound, and quickly convergent. First, the authors evaluate the performance of the presented HTGA approach by studying some global numerical optimization problems. Then, the presented HTGA approach is effectively applied to solve three examples on forecasting the sunspot numbers, tuning the associative memory, and solving the XOR problem. The numbers of hidden nodes and the links of the feedforward neural network are chosen by increasing them from small numbers until the learning performance is good enough. As a result, a partially connected feedforward neural network can be obtained after tuning. This implies that the cost of implementation of the neural network can be reduced. In these studied problems of tuning both network structure and parameters of a feedforward neural network, there are many parameters and numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the presented HTGA approach can obtain better results than the existing method reported recently in the literature. [ABSTRACT FROM AUTHOR]

Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results. [ABSTRACT FROM AUTHOR]

This paper presents an improved genetic algorithm with multiplier updating (IGA•MU) to solve power economic dispatch (PED) problems of units with valve-point effects and multiple fuels. The proposed IGA•MU integrates the improved genetic algorithm (IGA) and the multiplier updating (MU). The IGA equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions, and the MU is employed to handle the equality and inequality constraints of the PED problem. Few FED problem-related studies have seldom addressed both valve-point loadings and change fuels. To show the advantages of the proposed algorithm, which was applied to test FED problems with one example considering valve-point effects, one example considering multiple fuels, and one example addressing both valve-point effects and multiple fuels. Additionally, the proposed algorithm was compared with previous methods and the conventional genetic algorithm (CGA) with the MU (CGA•MU), revealing that the proposed IGA•MU is more effective than previous approaches, and applies the realistic PED problem more efficiently than does the CGA•MU. Especially, the proposed algorithm is highly promising for the large-scale system of the actual FED operation. [ABSTRACT FROM AUTHOR]

This paper describes the synthesis method of linear array geometry with minimum sidelobe level and null control using the particle swarm optimization (PSO) algorithm. The PSO algorithm is a newly discovered, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the P50 algorithm is much easier to understand and implement and requires the least of mathematical preprocessing. The array geometry synthesis is first formulated as an optimization problem with the goal of sidelobe level (SLL) suppression and/or null placement in certain directions, and then solved by the PSO algorithm for the optimum element locations. Three design examples are presented that illustrate the use of the PSO algorithm, and the optimization goal in each example is easily achieved. The results of the P50 algorithm are validated by comparing with results obtained using the quadratic programming method (QPM). [ABSTRACT FROM AUTHOR]

An optimization method, such as the steepest gradient methods, could not easily obtain globally optimum solutions for devising antenna design parameters that allow the antenna to simultaneously improve multiple performances such as gain, sidelobe level, and input impedance. The genetic algorithm (GA) is suitable for empirically solving optimization problems and is effective in designing an antenna. In particular, this method can solve the multi- objective optimization problem using various Pareto-optimal solutions in an extremely efficient manner. In this paper, the Pareto GA, by which various Pareto-optimal solutions for each objective function (performance) can be obtained in a single trial of a numerical simulation and which enables the selection of parameters in accordance with the design requirement, is applied to the multiobjective optimization design of the Yagi-Uda antenna. The effectiveness of the Pareto GA was demonstrated by comparing the performances obtained by the Pareto GA with those of the previously reported values, which were obtained by the conventional GA, and with the values of the design benchmark reference. [ABSTRACT FROM AUTHOR]

The dominating contribution of interconnect to system performance has made it critical to plan the resources of the buffers and routes in the early stage of the layout In this paper, we integrate floorplanning with buffer insertion for performance-driven design processes. We devise a two-step method to evaluate the feasible buffer insertion sites, which can improve the efficiency of the buffer-planning algorithm. By partitioning all empty spaces into blocks in the packing process, the buffer allocation is handled as an integral part of the floorplanning. Our buffer-planning algorithm maps the buffers into tiles with consideration of routing congestion. In this approach, we construct a distribution graph to model the possible routes. The buffer allocation method is performed on the updated distribution graph to find the buffer locations with their respective congestion costs. The method is based on a simulated annealing approach, which is composed of multiple phases to speed up the optimization. Since there is more freedom with floorplan optimization, the empirical results demonstrate better performance. [ABSTRACT FROM AUTHOR]

A methodology is presented for the design synthesis of metamaterial ferrites, or metaferrites, that retain their desirable magnetic properties at frequencies above 1 GHz. The design syn- thesis is accomplished by optimizing a high impedance frequency selective surface (HZ-FSS) structure via a genetic algorithm (GA) for the desired effective permeability of an equivalent magnetic substrate backed by a perfect electric conductor ground plane. The ability to optimize the design parameters of these HZ-FSS structures allows for the possibility of synthesizing low-loss dispersive metaferrites with either a positive or a negative real part of the effective permeability at the desired operating frequency band. The results presented in this paper demonstrate five possible metaferrite designs: two with the associated real and imaginary permeabilities for use as low-loss magnetic materials, and three designs for use as absorbing materials. [ABSTRACT FROM AUTHOR]

In this paper, a newly developed optimization algorithm, called the dynamic encoding algorithm for searches (DEAS), is introduced and applied to the parameter identification of an induction motor for vector control and fault detection. Digital simulations are conducted on startup with no load and normal operation with load perturbations. DEAS is compared with the continuous-time prediction error method and the genetic algorithm via identification performance using the startup signals. The capability of onload identification using the proposed technique is also verified with transient signals. Consequently, DEAS is shown to locate more precise parameter values than both the compared methods especially with much faster execution time than the genetical algorithm. [ABSTRACT FROM AUTHOR]