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Estimation of Distribution Algorithms (EDAs) are metaheuristics where learning a model and sampling new solutions replaces the variation operators recombination and mutation used in standard Genetic Algorithms. The choice of these models as well as the corresponding training processes are subject to the bias/variance tradeoff, also known as under- and overfitting: simple models cannot capture complex interactions between problem variables, whereas complex models are susceptible to modeling random noise. This paper suggests using Denoising Autoencoders (DAEs) as generative models within EDAs (DAE-EDA). The resulting DAE-EDA is able to model complex probability distributions. Furthermore, overfitting is less harmful, since DAEs overfit by learning the identity function. This over-fitting behavior introduces unbiased random noise into the samples, which is no major problem for the EDA but just leads to higher population diversity. As a result, DAE-EDA runs for more generations before convergence and searches promising parts of the solution space more thoroughly. We study the performance of DAE-EDA on several combinatorial single-objective optimization problems. In comparison to the Bayesian Optimization Algorithm, DAE-EDA requires a similar number of evaluations of the objective function but is much faster and can be parallelized efficiently, making it the preferred choice especially for large and difficult optimization problems. [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]

The paper presents a genetic algorithm for multicriteria optimization of parameters of a technological process, which finds a solution not from a certain point but from a specified population. The objective functions are used without their derivatives and probabilistic choice rules are applied. The multicriteria optimization is based on finding a solution that simultaneously optimizes the machining parameters defined by functions of productivity, tool consumption, tool cost, etc. [ABSTRACT FROM AUTHOR]

Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of particle swarm optimization (PSO) to the programming of pavement maintenance activities at the network level. Furthermore, the application of the PSO technique and its relevance to solving the programming problem in a pavement management system (PMS) is discussed. The robustness and quick search capability of PSO enables it to effectively handle the highly constrained problem of pavement management activities programming, which has an extremely large solution space of astronomical scale. Examples are presented to highlight the versatility of PSO in accommodating different forms of objective functions and comparing the results with the genetic algorithm (GA). This paper compares PSO and GA with respect to rate of convergence and accuracy of modeling PMS using an example problem. The results of this paper confirmed the potential of PSO to successfully model the PMS. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, heuristic algorithms such as simulated annealing (SA), genetic algorithm (GA) and hybrid algorithm (hybrid-GASA) were applied to tool-path optimization problem for minimizing airtime during machining. Many forms of SA rely on random starting points that often give poor solutions. The problem of how to efficiently provide good initial estimates of solution sets automatically is still an ongoing research topic. This paper proposes a hybrid approach in which GA provides a good initial solution for SA runs. These three algorithms were tested on three-axis-cartesian robot during milling of wood materials. Their performances were compared based on minimum path and consequently minimum airtime. In order to make a comparison between these algorithms, two cases among the several milling operations were given here. According to results obtained from these examples, hybrid algorithm gives better results than other heuristic algorithms alone. Due to combined global search feature of GA and local search feature of SA, hybrid approach using GA and SA produces about 1.5% better minimum path solutions than standard GA and 47% better minimum path solutions than standard SA. [Copyright &y& Elsevier]

Abstract: This paper presents a study on design optimization of multi-state weighted k-out-of-n systems. The studied system reliability model is more general than the traditional k-out-of-n system model. The system and its components are capable of assuming a whole range of performance levels, varying from perfect functioning to complete failure. A utility value corresponding to each state is used to indicate the corresponding performance level. A widely studied reliability optimization problem is the “component selection problem”, which involves selection of components with known reliability and cost characteristics. Less adequately addressed has been the problem of determining system cost and utility based on the relationships between component reliability, cost and utility. This paper addresses this topic. All the optimization problems dealt with in this paper can be categorized as either minimizing the expected total system cost subject to system reliability requirements, or maximizing system reliability subject to total system cost limitation. The resulting optimization problems are too complicated to be solved by traditional optimization approaches; therefore, genetic algorithm (GA) is used to solve them. Our results show that GA is a powerful tool for solving these kinds of problems. [Copyright &y& Elsevier]

In this paper, work concerning robust D-stability analysis is considered for discrete descriptor systems with structured uncertainties and multiple time delays. A genetic algorithm is used to make the stability test of the main result more efficient. A delay-dependent robust D-stability criterion is proposed to ensure that all poles of the discrete multiple time-delay descriptor systems subject to uncertainties are located within a disk contained in the unit circle. It will be seen that the system considered in this paper is more general and the proposed robust D-stability criterion is less conservative and easier to check than those presented in recent research. Moreover, a delay-dependent criterion is derived to guarantee that the system is regular, impulse-free, and D-stable. Since the boundary test occurs during the check of the criteria, a genetic algorithm is used to make it more precise. Finally, an example is introduced to illustrate our main results. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new procedure for the optimization of the mooring design of floating platforms, in which an automatic design sequence is also established. Regarding the optimization philosophy, the following aspects are dealt with: [•] The optimization of the platform heading and its mooring pattern, taking into account the environmental force spreading; [•] optimum line length or line tension for each mooring line, associated to the optimization of the mooring line materials and sizes. Basically, the main goal of this paper is to introduce a new method, which will provide the quickest way to find the best mooring system, defined here as that which minimizes platform responses. A genetic algorithm (GA) is applied in this contribution, and this paper describes exactly the procedure of developing a GA code directed toward the solution of mooring design optimization problems. In order to prove the efficiency and the vast potential of the proposed algorithm as a design tool, sample moorings are analyzed for different environmental conditions and the final results, including the time required to run them, are presented. [Copyright &y& Elsevier]

This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an improvement of an efficient multiobjective optimization algorithm, Non-dominated Sorting Genetic Algorithm II, NSGA-II, that has been here applied to solve the problem of optimal capacitors placement in distribution systems. The studied improvement involves the Crowded Comparison Operator and modifies it in order to handle several constraints. The problem of optimal location and sizing of capacitor banks for losses reduction and voltage profile flattening in medium voltage (MV) automated distribution systems is a difficult combinatorial constrained optimization problem which is deeply studied in literature. In this paper, the efficiency of the proposed Crowded Comparison Operator, CCO1, is compared to the efficiency of another Crowded Comparison Operator, CCO2, whose definition derives from the constraint-domination principle proposed by Deb et al. The two operators are tested on difficult test problems as well as on the optimal capacitors placement problem. [Copyright &y& Elsevier]

This paper presents a comparative study of two indirect solution representations, a generative and an ontogenic one, on a set of well-known 2D truss design problems. The generative representation encodes the parameters of a trusses design as a mapping from a 2D space. The ontogenic representation encodes truss design parameters as a local truss transformation iterated several times, starting from a trivial initial truss. Both representations are tested with a naive evolution strategy based optimization scheme, as well as the state of the art HyperNEAT approach. We focus both on the best objective value obtained and the computational cost to reach a given level of optimality. The study shows that the two solution representations behave very differently. For experimental settings with equal complexity, with the same optimization scheme and settings, the generative representation provides results which are far from optimal, whereas the ontogenic representation delivers near-optimal solutions. The ontogenic representation is also much less computationally expensive than a direct representation until very close to the global optimum. The study questions the scalability of the generative representations, while the results for the ontogenic representation display much better scalability. [ABSTRACT FROM AUTHOR]

Stacking-sequence optimization of laminated composite structures is a combinatorial problem involving discrete variables when ply angles are used as design variables. Such an optimization problem can be solved using genetic algorithms coupled with response surface methods to reduce the computational cost. Alternatively, the optimization problem can be formulated using lamination parameters as design variables, allowing efficient gradient-based optimization methods to be used. Nevertheless, practical stacking sequences have to be reconstructed in a second step, which can be problematic. A synthesis of the two-step approach and the response surface approach to composite design is proposed in this paper. The synthesis is effected by constructing a new multipoint structural approximation scheme based on an innovative use of the classical Shepard's method applied in the space of laminate stiffness. A distance measure in the stiffness space is proposed to adapt the method to composite design. The response surface genetic algorithm optimization is performed starting from a continuous optimum defined by prior continuous optimization. The method is first applied to single-laminate design example problems to demonstrate its efficiency and accuracy compared with known methods. The capacity of the optimization strategy to solve more complex problems is then demonstrated using two patch design examples. [ABSTRACT FROM AUTHOR]

Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of mainly binary optimization problems. In this paper, we introduce the affinity propagation EDA (AffEDA) which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and nonbinary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased. [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]

Turning points prediction has long been a tough task in the field of time series analysis due to its strong nonlinearity, and thus has attracted many research efforts. In this study, the turning points prediction (TPP) framework is presented and further employed to develop a novel trading strategy designing approach to financial investment. The TPP framework is a machine learning-based solution incorporating chaotic dynamic analysis and neural network modeling. It works on the ground of a nonlinear mapping deduced in financial time series through chaotic analysis. An event characterization method is created in TTP framework to characterize trend patterns in ongoing financial time series. The main contributions of this paper are (1) it presents an ensemble learning based TPP framework, within which the nonlinear mapping is approximated by the ensemble artificial neural network (EANN) model with a new parameters learning algorithm; (2) a genetic algorithm (GA) based threshold optimization procedure is described with a newly defined performance measure, named TpMSE, which is used as a cost function; and (3) a trading strategy designing approach is proposed based on the TPP framework. The proposed approach was applied to the two real-world financial time series, i.e., an individual stock quote time series and the Dow Jones Industrial Average (DJIA) index time series. Experimental results show that the proposed approach can help investors make profitable decisions. [Copyright &y& Elsevier]

In this paper we propose a hybrid algorithm to optimize the structure of TSK type fuzzy model using backpropagation (BP) learning algorithm and non-dominated sorting genetic algorithm (NSGA-II). In a first step, BP algorithm is used to optimize the parameters of the model (parameters of membership functions and fuzzy rules). NSGA-II is used in a second phase, to optimize the number of fuzzy rules and to fine tune the parameters. A well known benchmark is used to evaluate performances of the proposed modelling approach, and compare it with other modelling approaches. [Copyright &y& Elsevier]

Abstract: TSP (traveling salesman problem) is one of the typical NP-hard problems in combinatorial optimization problem. An improved genetic algorithm with reinforcement mutation, named RMGA, was proposed to solve the TSP in this paper. The core of RMGA lies in the use of heterogeneous pairing selection instead of random pairing selection in EAX and the construction of reinforcement mutation operator, named RL-M, by modifying the Q-learning algorithm and applying it to those individual generated from modified EAX. The experimental results on small and large size TSP instances in TSPLIB (traveling salesman problem library) have shown that RMGA could almost get optimal tour every time in reasonable time and thus outperformed the known EAX-GA and LKH in the quality of solutions and the running time. [Copyright &y& Elsevier]

Abstract  Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years. In this paper, an efficient multiobjective differential evolution algorithm is presented for engineering design. Our proposed approach adopts the orthogonal design method with quantization technique to generate the initial archive and evolutionary population. An archive (or secondary population) is employed to keep the nondominated solutions found and it is updated by a new relaxed form of Pareto dominance, called Pareto-adaptive ϵ-dominance (paϵ-dominance), at each generation. In addition, in order to guarantee to be the best performance produced, we propose a new hybrid selection mechanism to allow the archive solutions to take part in the generating process. To handle the constraints, a new constraint-handling method is employed, which does not need any parameters to be tuned for constraint handling. The proposed approach is tested on seven benchmark constrained problems to illustrate the capabilities of the algorithm in handling mathematically complex problems. Furthermore, four well-studied engineering design optimization problems are solved to illustrate the efficiency and applicability of the algorithm for multiobjective design optimization. Compared with Nondominated Sorting Genetic Algorithm II, one of the best MOEAs available at present, the results demonstrate that our approach is found to be statistically competitive. Moreover, the proposed approach is very efficient and is capable of yielding a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts. [ABSTRACT FROM AUTHOR]

When solving multi-objective optimization problems subject to constraints in reliability-based design, it is desirable for the decision maker to have a sufficient number of solutions available for selection. However, many existing approaches either combine multiple objectives into a single objective or treat the objectives as penalties. This results in fewer optimal solutions than would be provided by a multi-objective approach. For such cases, a niched Pareto Genetic Algorithm (GA) may be a viable alternative. Unfortunately, it is often difficult to set penalty parameters that are required in these algorithms. In this paper, a multi-objective optimization algorithm is proposed that combines a niched Pareto GA with a constraint handling method that does not need penalty parameters. The proposed algorithm is based on Pareto tournament and equivalence sharing, and involves the following components: search for feasible solutions, selection of non-dominated solutions and maintenance of diversified solutions. It deals with multiple objectives by incorporating the concept of Pareto dominance in its selection operator while applying a niching pressure to spread the population along the Pareto frontier. To demonstrate the performance of the proposed algorithm, a test problem is presented and the solution distributions in three different generations of the algorithm are illustrated. The optimal solutions obtained with the proposed algorithm for a practical reliability problem are compared with those obtained by a single-objective optimization method, a multi-objective GA method, and a hybrid GA method. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the dimensional synthesis of a 3-RPS (revolute–prismatic–spherical) parallel manipulator according to the limitation on the range of motion of the spherical joints. The synthesis of the manipulator is carried out using a hybrid optimization method called GA–simplex method. The architectural parameters of the manipulator are determined by considering the design problem as an optimization problem. The hybrid method first carries out the global search for the solution using genetic algorithm (GA) and then applies the simplex method for the local search. While determining the dimensions of the fixed platform, the physical constraints such as limitation on the range of motion of the spherical joints are considered to make the design practical. The procedure presented can be applied to synthesize the manipulator for any number of positions. A numerical example for the synthesis with 10 positions is presented. [Copyright &y& Elsevier]

Abstract: In this paper, one novel genetic algorithm dynamic chain-like agent genetic algorithm (CAGA) is proposed for solving global numerical optimization problem and feature selection problem. The CAGA combines the chain-like agent structure with dynamic neighboring genetic operators to get higher optimization capability. An agent in chain-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and can use knowledge to increase energies. Global numerical optimization problem and feature selection problem are the most important problems for evolutionary algorithm, especially for genetic algorithm. Hence, the experiments of global numerical optimization and feature selection are necessary to verify the performance of genetic algorithms. Corresponding experiments have been done and show that CAGA is suitable for real coding and binary coding optimization problems, and has more precise and more stable optimization results. [Copyright &y& Elsevier]

Abstract: Outdoor thermal environments represented by the urban heat-island phenomenon that accompanies urbanization have become markedly worse in recent years during summer in Japan. Many researches have been conducted into the effect of plants on the outdoor thermal environment during summer. However, the optimum design system for such plants has not been established yet. In this paper, (1) a multi-objective optimum design method for a comfortable outdoor thermal urban environment is developed using a multi-objective genetic algorithm (MOGA); and (2) in order to examine the validity of the proposed optimum design method, two cases are analyzed: a single-objective optimization problem from the viewpoint of human''s comfort and a multi-objective optimization problem involving various factors, such as the landscape, outdoor thermal environment, and economy. The results of this method show the trade-off relationship between two or more objective functions, and the Pareto-optimal solution set is achieved. [Copyright &y& Elsevier]

Weld lines are one of the critical defects in injection molding production. They affect the strength of the plastic parts and are visually unpleasant. To address this problem, this paper proposes some strategies and methods for the formulation and implementation of an optimization procedure to minimize the defects of weld lines by minimizing their total length and by positioning them as further away as possible from, or restricting them from occurring at, the specified critical regions on the part surface. Several concepts are defined, such as the weld line relevant part qualities, types of critical regions, and constraints on the occurrence of the weld lines. Based on these, the optimization problem is modeled and a genetic algorithm (GA) based optimization procedure is proposed. The methodology can provide useful guidelines for injection molding designers in achieving the optimal part quality relevant to the weld lines. A case study is also conducted to demonstrate the effectiveness of the methodology. [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]

Ontology matching is a process for selection of a good alignment across entities of two (or more) ontologies. This can be viewed as a two-phase process of (1) applying a similarity measure to find the correspondence of each pair of entities from two ontologies, and (2) extraction of an optimal or near optimal mapping. This paper is focused on the second phase and introduces our evolutionary approach for that. To be able to do so, we need a mechanism to score different possible mappings. Our solution is a weighting mechanism named coincidence-based weighting. A genetic algorithm is then introduced to create better mappings in successive iterations. We will explain how we code a mapping as well as our crossover and mutation functions. Evaluation of the algorithm is shown and discussed. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, system network planning expansion is formulated for mixed integer programming, a genetic algorithm (GA) and tabu search (TS). Compared with other optimization methods, GAs are suitable for traversing large search spaces, since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will tend to become more common as the search space increases in size. GA’s give an excellent trade off between solution quality and computing time and flexibility for taking into account specific constraints in real situations. TS has emerged as a new, highly efficient, search paradigm for finding quality solutions to combinatorial problems. It is characterized by gathering knowledge during the search and subsequently profiting from this knowledge. The attractiveness of the technique comes from its ability to escape local optimality. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. The DC load flow equations for the network are embedded in the constraints of the mathematical model to avoid sub-optimal solutions that can arise if the enforcement of such constraints is done in an indirect way. The solution of the model gives the best line additions and also provides information regarding the optimal generation at each generation point. This method of solution is demonstrated on the expansion of a 10 bus bar system to 18 bus bars. Finally, a steady-state genetic algorithm is employed rather than generational replacement, also uniform crossover is used. [Copyright &y& Elsevier]

Abstract: Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search algorithms can converge in a few iterations but lack a global perspective. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages. This paper proposes a new hybrid optimization technique that merges a genetic algorithm with a local search strategy based on the interior point method. The efficiency of this hybrid approach is demonstrated by solving a constrained multi-objective mathematical test-case. [Copyright &y& Elsevier]

Robust optimization searches for optimal solutions which remain feasible under the effect of uncertainties. The conventional robust optimization, however, often requires great computational cost to find robust solutions. This paper introduces a novel robust optimization method based on genetic algorithm. In this method, individuals with "good" performance are selected and tested for their robustness for reduction of computational time. It is shown that the present method works in shorter time in comparison with the conventional robust optimization. [ABSTRACT FROM AUTHOR]

Abstract: Multi-criteria human resource allocation involves deciding how to divide human resource of limited availability among multiple demands in a way that optimizes current objectives. In this paper, we focus on multi-criteria human resource allocation for solving multistage combinatorial optimization problem. Hence we tackle this problem via a multistage decision-making model. A multistage decision-making model is similar to a complex problem solving, in which a suitable sequence of decisions is to be found. The task can be interpreted as a series of interactions between a decision maker and an outside world, at each stage of which some decisions are available and their immediate effect can be easily computed. Eventually, goals would be reached due to the found of optimized variables. In order to obtain a set of Pareto solutions efficiently, we propose a multiobjective hybrid genetic algorithm (mohGA) approach based on the multistage decision-making model for solving combinatorial optimization problems. According to the proposed method, we apply the mohGA to seek feasible solutions for all stages. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multiobjective resource allocation problem. [Copyright &y& Elsevier]

Abstract: In this paper, a new hybrid genetic algorithm is proposed, which combines the genetic algorithm with hill-climbing search steps differently from some former algorithms. The new algorithm can be widely applied to a class of global optimization problems for continuous functions with box constraints. Finally, numerical examples show that this algorithm can yield the global optimum with high efficiency. [Copyright &y& Elsevier]

Many techniques have been reported for handwriting-based writer identification. None of these techniques assume that the written text is in Arabic. In this paper we present a new technique for feature extraction based on hybrid spectral-statistical measures (SSMs) of texture. We show its effectiveness compared with multiple-channel (Gabor) filters and the grey-level co-occurrence matrix (GLCM), which are well-known techniques yielding a high performance in writer identification in Roman handwriting. Texture features were extracted for wide range of frequency and orientation because of the nature of the spread of Arabic handwriting compared with Roman handwriting, and the most discriminant features were selected with a model for feature selection using hybrid support vector machine-genetic algorithm techniques. Four classification techniques were used: linear discriminant classifier (LDC), support vector machine (SVM), weighted Euclidean distance (WED), and the K nearest neighbours (K_NN) classifier. Experiments were performed using Arabic handwriting samples from 20 different people and very promising results of 90.0% correct identification were achieved. [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]

Abstract: This paper proposes a recurrent fuzzy network design using the hybridization of a multigroup genetic algorithm and particle swarm optimization (R-MGAPSO). The recurrent fuzzy network designed here is the Takagi–Sugeno–Kang (TSK)-type recurrent fuzzy network (TRFN), in which each fuzzy rule comprises spatial and temporal sub-rules. Both the number of fuzzy rules and the parameters in a TRFN are designed simultaneously by R-MGAPSO. In R-MGAPSO, the techniques of variable-length individuals and the local version of particle swarm optimization are incorporated into a genetic algorithm, where individuals with the same length constitute the same group, and there are multigroups in a population. Population evolution consists of three major operations: elite enhancement by particle swarm optimization, sub-rule alignment-based crossover, and mutation. To verify the performance of R-MGAPSO, dynamic plant and a continuous-stirred tank reactor controls are simulated. R-MGAPSO performance is also compared with genetic algorithms in these simulations. [Copyright &y& Elsevier]

A cost effective channel section for a specified flow rate, roughness coefficients, longitudinal slope, and various cost parameters can be determined using an optimization technique. However, the derived optimal channel section may not be feasible for construction because of in situ conditions. The local soil conditions may not support the optimal side slope of the channel and if constructed, the slope may fail. It is therefore necessary to also incorporate the criteria for side slope stability in designing an optimal open channel section. In this paper, a new methodology has been developed to design a stable and optimal channel section using hybrid optimization techniques. A genetic algorithm based optimization model is developed initially to determine the factor of safety of a channel slope for given soil parameters. This optimization model is then externally linked with a separate sequential quadratic programming based optimization model to evaluate the parameters of the stable and optimal channel section. Solution for various example problems incorporating different soil parameters are illustrated to demonstrate the applicability of the developed methodology. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a solution technique for finding the optimum location and sizing of the shunt compensation devices in transmission systems. The objective of the formulation is to improve the voltage stability of the system while maintaining acceptable voltage profile. The problem can be formulated as an integer nonlinear optimization problem. The newly developed evolutionary technique particle swarm optimization (PSO) is used to solve this problem. Case studies with the Ward–Hale 6-bus, IEEE 14-bus and 30-bus systems are presented to illustrate the applicability of the algorithm. A comparison with the genetic algorithm (GA) is performed to show the quality of the solutions obtained by PSO. [Copyright &y& Elsevier]

Abstract: In the fault diagnosis based on support vector machines (SVM), irrelevant variables in the fault samples spoil the performance of the SVM classifier and reduce the recognition accuracy. On the other hand, some SVM parameters are usually selected artificially, which hampers the efficiency of the SVM algorithm in practical applications. A new method that jointly optimises the feature selection and the SVM parameters with a modified discrete particle swarm optimisation is presented in this paper. A correct ratio based on a new evaluation method is used to estimate the performance of the SVM, and serves as the target function in the optimisation problem. A hybrid vector that describes both the fault features and the SVM parameters is taken as the constraint condition. This new method can select the best fault features in a shorter time, and improves the performance of the SVM classifier, and has fewer errors and a better real-time capacity than the method based on principal component analysis (PCA) and SVM, or the method based on Genetic Algorithm (GA) and SVM, as shown in the application of fault diagnosis of the turbo pump rotor. [Copyright &y& Elsevier]

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]

Habitat fragmentation and habitat loss are causing many species to become locally extinct. The need to restore or conserve biodiversity has initiated plans in Europe—and, more specifically, in the Netherlands—to create networks of nature areas for the improvement of spatial coherence. In this paper spatial coherence is measured by the total boundary length of all nature areas. Spatial optimization is used to optimize the spatial coherence, in order to evaluate the so-called Dutch 2003 plans. The goal of the optimization is therefore to minimize the total sum of boundary lengths for all nature areas, while increasing the total surface area. Potential configurations have been calculated and compared with the 2003 plans, with respect to boundary length and the size of nature areas. The calculations have been performed by allocating increasing amounts of new nature to test if smaller numbers of hectares could yield a comparable perimeter decline. Optimization has been carried out separately (per province) and collectively (for all the Dutch provinces together). So-called genetic algorithms were used to tackle this strongly nonlinear problem. An interesting feature of the method used is its ability to optimize more than half a million variables—that is, potential nature cells. Allocating only half the area allocated in the 2003 plans resulted in a boundary-length decline of 30% compared with the 2003 plans, and yielded many more coherent areas than are found in the 2003 plans. Compared with the optimization per province, the collective optimization resulted nationwide in larger nature areas, across provincial borders. Considering the original aims, we can conclude that carrying out the 2003 plans will not improve the overall spatial coherence in terms either of the boundary length or of the number of areas and total surface area in the largest surface class. An additional advantage of the heuristic method discussed is that slightly different configurations can be found with almost the same characteristics in a relative short time. This will allow the provinces to choose the most suitable configuration. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a meta-heuristic algorithm, variable neighborhood search (VNS), to the redundancy allocation problem (RAP). The RAP, an NP-hard problem, has attracted the attention of much prior research, generally in a restricted form where each subsystem must consist of identical components. The newer meta-heuristic methods overcome this limitation and offer a practical way to solve large instances of the relaxed RAP where different components can be used in parallel. Authors’ previously published work has shown promise for the variable neighborhood descent (VND) method, the simplest version among VNS variations, on RAP. The variable neighborhood search method itself has not been used in reliability design, yet it is a method that fits those combinatorial problems with potential neighborhood structures, as in the case of the RAP. Therefore, authors further extended their work to develop a VNS algorithm for the RAP and tested a set of well-known benchmark problems from the literature. Results on 33 test instances ranging from less to severely constrained conditions show that the variable neighborhood search method improves the performance of VND and provides a competitive solution quality at economically computational expense in comparison with the best-known heuristics including ant colony optimization, genetic algorithm, and tabu search. [Copyright &y& Elsevier]

Abstract: In this paper we propose a new approach in genetic algorithm called distributed hierarchical genetic algorithm (DHGA) for optimization and pattern matching. It is eventually a hybrid technique combining the advantages of both distributed and hierarchical processes in exploring the search space. The search is initially distributed over the space and then in each subspace the algorithm works in a hierarchical way. The entire space is essentially partitioned into a number of subspaces depending on the dimensionality of the space. This is done in order to spread the search process more evenly over the whole space. In each subspace the genetic algorithm is employed for searching and the search process advances from one hypercube to a neighboring hypercube hierarchically depending on the convergence status of the population and the solution obtained so far. The dimension of the hypercube and the resolution of the search space are altered with iterations. Thus the search process passes through variable resolution (coarse-to-fine) search space. Both analytical and empirical studies have been carried out to evaluate the performance between DHGA and distributed conventional GA (DCGA) for different function optimization problems. Further, the performance of the algorithms is demonstrated on problems like pattern matching and object matching with edge map. [Copyright &y& Elsevier]

Abstract: Studies indicate that RNA may enter intermediate and multiple conformational states, which may impact gene expression and molecular function. It is known that the biologically functional states of RNA molecules may not correspond to their minimum energy conformations, that kinetic barriers may trap the molecule in a local minimum, that folding often occurs during transcription, and that cases exist in which a molecule will transition between one or more functional conformations. Thus, methods for simulating the folding pathway and dynamic behavior of an RNA molecule are important for the prediction of RNA structure and its associated functions. We have developed several data mining techniques guided by interactive visualization tools associated with our massively parallel genetic algorithm for RNA/DNA secondary structure prediction, MPGAfold, and StructureLab analysis workbench. Most of the methods and tools are also applicable to dynamic programming algorithm (DPA) folding data analysis. When applied to MPGAfold results these methodologies are used to determine the significant intermediate and final structures associated with co-transcriptional and full length RNA folding. Since the genetic algorithm is essentially stochastic, multiple runs are required to develop a consensus understanding of an RNA structure. The interactive visualizations facilitate interpretation of results from sequential or full length individual MPGAfold runs, final results of multiple folding runs, including multiple population sizes, and the results from multiple RNA sequences of one family. This paper describes several of these techniques and shows how they are used to help solve this highly combinatoric problem. [Copyright &y& Elsevier]

Abstract: Over the past few years, researchers have developed a number of multiobjective evolutionary algorithms (MOEAs). Although most studies concentrate on solving unconstrained optimization problems, there exit a few studies where MOEAs have been extended to solve constrained optimization problems. Most of them were based on penalty functions for handling nonlinear constraints by genetic algorithms. However the performance of these methods is highly problem-dependent, many methods require additional tuning of several parameters. In this paper, we present a new optimization algorithm, which is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. The algorithm maintains a finite-sized archive of nondominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. The use of ε-dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto set approximation by choosing an appropriate ε value, which guarantees convergence and diversity. The results, provided by the proposed algorithm for six benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems. [Copyright &y& Elsevier]

Abstract: Although statistical modeling is a common task in different fields of science, it is still difficult to estimate the best model that can accurately describe inherent characteristics of a system for which historical or experimental data are available. Since we may classify estimating techniques as optimizations, we can model this problem as an optimization problem and solve it by a new heuristic algorithm like neural networks, genetic algorithms, and tabu search or by classic ones such as regression and econometric models. In this paper, we propose a new type of genetic algorithm to find the best regression model among all suggested and evaluate its performances by an economical case study. [Copyright &y& Elsevier]

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]