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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 proposes an approach using quality loss functions and multicriteria optimization procedure to determine appropriate process parameter values for producing quality products in a continuous casting system. Quality of a continuously cast product depends on the process conditions during the casting process. From the information available in the literature and industry, 17 critical quality conditions have been identified which have to be satisfied to prevent defect formation during casting. Each of these process conditions are connected to one or more than one process parameters and the process parameter values are to be selected such that all these 17 quality conditions are satisfied simultaneously. The optimization procedure suggested in the present study involves representing the relationship between the responses and the process variables in the form of an objective function and finding the best set of values for the process variables. The objective function is formulated as a loss function, which establishes a functional relationship between the input variables or process parameters and the quality criteria. The optimum values of the process parameters were obtained by minimizing the loss function using simulated annealing optimization algorithm. The details are presented in this paper with the help of a specific case. [Copyright &y& Elsevier]

Drawing graphs on a 2D grid with prescribed size is NP-complete, even if we restrict ourselves to planar orthogonal grid drawings of trees. In this paper, we introduce a new algorithm for polyline drawing of free trees on 2D grids which are bounded by rectilinear polygons. Our algorithm uses straight skeleton and simulated annealing and aims to distribute the vertices of the given trees uniformly on the given bounded grids. Our experimental results show that our algorithm is relatively successful to achieve uniform distribution of the nodes of the given trees. To our knowledge, this paper is the first attempt to develop algorithms that draw trees on restricted 2D grids which are bounded by simple rectilinear polygons. † E-mail: razzazi@ce.aut.ac.ir [ABSTRACT FROM AUTHOR]

The facility layout design problem is an extensively studied research problem and belongs to nonpolynomial hard (NP-hard) combinatorial optimization problem. Quadratic assignment problem (QAP) is one of the formulations that is investigated for facility layout design because of its wide applicability. Ant colony optimization (ACO), a biologically inspired heuristic has centered on solving the QAP by achieving approximation as good as possible. This paper presents a population-based hybrid ant system (PHAS), which is an extension of the hybrid ant system (HAS) in which the size of the ant colony has been fixed. The performance of the proposed ant algorithm for QAP is compared with the existing metaheuristic implementations such as tabu search, reactive tabu search, simulated annealing, genetic hybrid method, HAS, and max–min ant system. The experimental results show that the proposed PHAS perform significantly better than the other existing algorithms of QAP. [ABSTRACT FROM AUTHOR]

Recent research shows that simulated annealing with orthogonal array based neighbourhood functions can help in the search for a solution to a parametrical problem which is closer to an optimum when compared with conventional simulated annealing. Previous studies of simulated annealing analyzed only the main effects of variables of parametrical problems. In fact, both main effects of variables and interactions between variables should be considered, since interactions between variables exist in many parametrical problems. In this paper, an improved orthogonal array based neighbourhood function (IONF) for simulated annealing with the consideration of interaction effects between variables is described. After solving a set of parametrical benchmark function problems where interaction effects between variables exist, results of the benchmark tests show that the proposed simulated annealing algorithm with the IONF outperforms significantly both the simulated annealing algorithms with the existing orthogonal array based neighbourhood functions and the standard neighbourhood functions. Finally, the improved orthogonal array based simulated annealing was applied on the optimization of emulsified dynamite packing-machine design by which the applicability of the algorithm in real world problems can be evaluated and its effectiveness can be further validated. [Copyright &y& Elsevier]

Abstract: Simulated annealing method has been successfully applied to various combinatorial optimization problems. In the conventional simulated annealing, temperature and local search repetition are determined by simple algorithms with a higher transition probability in the beginning of the search and lower probability toward the end of the search. But these simple methods can cause inefficient search process. In order to overcome this defect, this paper provides an adaptive simulated annealing algorithm using fuzzy logic controller (FLC). FLC can control the temperature and the local search repetition of simulated annealing, thereby making the search process of simulated annealing more efficient. The performance of the proposed method is evaluated and favorably compared with the conventional simulated annealing through traveling salesman problem and equal piles problem. [Copyright &y& Elsevier]

Abstract: This paper presents an optimization algorithm based on Simulated Annealing. The algorithm – denoted as CMLPSA (Corrected Multi-Level &Multi-Point Simulated Annealing) – implements an advanced search mechanism where each candidate design is selected from a population of trial points randomly generated. Therefore, CMLPSA is in principle similar to meta-heuristic algorithms dealing with a pool/population of designs rather than with a single trial point such as it is usually done in classical simulated annealing. The multi-point strategy is adopted for both feasible and infeasible intermediate designs. In the former case, perturbations given to optimization variables are forced to follow the current rate of change exhibited by the cost function. In the latter case, 4th order approximate line search is performed in the neighbourhood of each feasible trial point generated in the current annealing cycle. Furthermore, CMLPSA includes a multi-level annealing strategy where trial points are generated by perturbing all design variables simultaneously (global level) or one by one (local level). Global or local search is performed basing on the current trend seen in the optimization process. CMLPSA is tested in six structural optimization problems where the objective is to minimize the weight of bar trusses – with up to 200 elements – subject to constraints on nodal displacements, member stresses and critical buckling loads. Test cases include both sizing and lay-out optimization variables. The computationally most expensive problem has 200 design variables and 3500 optimization constraints. CMLPSA is compared with other state-of-the-art SA algorithms and advanced global optimization methods like Heuristic Particle Swarm Optimization (HPSO) and Harmony Search (HS) recently presented in literature. Numerical results clearly demonstrate efficiency and robustness of CMLPSA. In particular, CMLPSA found better designs than the other SA-based algorithms and converged much more quickly to the optimum than HPSO and HS. Furthermore, CMLPSA is insensitive to initial design. [Copyright &y& Elsevier]

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]

Abstract: This paper proposes the use of a bank of Hopfield networks to solve a class of constraints which appear in combinatorial optimization problems. Specifically, we deal with problems which constraints’ structure can be represented by a binary matrix C, and can be separated in independent substructures. We show that a bank of Hopfield networks can solve these constraints, and also can be easily hybridized with a global search algorithm, such as simulated annealing, to obtain a final solution to the problem. We apply our approach to the solution of a famous logic-type puzzle, the light-up puzzle, where we report improvements over a branch and bound algorithm, and to an important problem which arises in electronic control: the so-called Crossbar Switch Problem. [Copyright &y& Elsevier]

The no-wait flow shop scheduling that requires jobs to be processed without interruption between consecutive machines is a typical NP-hard combinatorial optimization problem, and represents an important area in production scheduling. This paper proposes an effective hybrid algorithm based on particle swarm optimization (PSO) for no-wait flow shop scheduling with the criterion to minimize the maximum completion time (makespan). In the algorithm, a novel encoding scheme based on random key representation is developed, and an efficient population initialization, an effective local search based on the Nawaz-Enscore-Ham (NEH) heuristic, as well as a local search based on simulated annealing (SA) with an adaptive meta-Lamarckian learning strategy are proposed and incorporated into PSO. Simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed hybrid algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Ant algorithm is a multi-agent systems inspired by the behaviors of real ant colonies function to solve optimization problems. In this paper an ant colony optimization algorithm is developed to solve the quadratic assignment problem. The local search process of the algorithm is simulated annealing. In the exploration of the search space, the evaluation of pheromones which are laid on the ground by ants is used. In this work, the algorithm is analyzed by using current problems in the literature and is compared with other metaheuristics. [Copyright &y& Elsevier]

Abstract: The minimum linear arrangement problem is widely used and studied in many practical and theoretical applications. In this paper we present a linear-time algorithm for the problem inspired by the algebraic multigrid approach which is based on weighted edge contraction rather than simple contraction. Our results turned out to be better than every known result in almost all cases, while the short running time of the algorithm enabled experiments with very large graphs. [Copyright &y& Elsevier]

This paper presents a mathematical stochastic methodology for transit route network optimization. The goal is to provide an effective computational tool for the optimization of a large-scale transit route network to minimize transfers with reasonable route directness while maximizing service coverage. The methodology includes representation of transit route network solution search spaces, representation of transit route and network constraints, and a stochastic search scheme based on an integrated simulated annealing and genetic algorithm solution search method. The methodology has been implemented as a computer program, tested using previously published results, and applied to a large-scale realistic network optimization problem. [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]

Abstract: Power system stability enhancement via STATCOM-based stabilizers is thoroughly investigated in this paper. This study presents a singular value decomposition (SVD)-based approach to assess and measure the controllability of the poorly damped electromechanical modes by STATCOM different control channels. The coordination among the proposed damping stabilizers and the STATCOM internal ac and dc voltage controllers has been taken into consideration. The design problem of STATCOM-based stabilizers is formulated as an optimization problem. For coordination purposes, a time domain-based multiobjective junction to improve the system stability as well as ac and dc voltage regulation is proposed. Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. This aims to enhance both rotor angle stability and voltage regulation of the power system. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions. It is also observed that the proposed STATCOM-based damping stabilizers extend the critical clearing time (CCT) and enhance greatly the power system transient stability. [Copyright &y& Elsevier]

In this paper, four simulated annealing based multiobjective algorithms—SMOSA, UMOSA, PSA and WMOSA have been used to solve multiobjective optimization of constrained problems with varying degree of complexity along with a new PDMOSA algorithm. PDMOSA algorithm uses a strategy of Pareto dominant based fitness in the acceptance criteria of simulated annealing and is improved. In all algorithms, the current solution explores its neighborhoods in a way similar to that of classical simulated annealing. The performance and computational cost for all algorithms have been studied. All algorithms are found to be quite robust with algorithmic parameters and are capable of generating a large number of well diversified Pareto-optimal solutions. The quality and diversification of Pareto-optimal solutions generated by all algorithms are found to be problem specific. The computational cost is least by WMOSA and is followed by PDMOSA. The algorithms are simple to formulate and require reasonable computational time. Hence, the simultaneous use of all algorithms is suggested to obtain a wider spectrum of efficient solutions. [Copyright &y& Elsevier]

This paper presents a procedural framework that unifies various mechanisms to look for discrete-neighborhood saddle points in solving discrete constrained optimization problems (DCOPs). Our approach is based on the necessary and sufficient condition on local optimality in discrete space, which shows the one-to-one correspondence between the discrete-space constrained local minima of a problem and the saddle points of the corresponding Lagrangian function. To look for such saddle points, we study various mechanisms for performing ascents of the Lagrangian function in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our results show that CSAEA, a combined constrained simulated annealing and evolutionary algorithm, performs well when using mutations and crossovers to generate trial points and accepting them based on the Metropolis probability. We apply iterative deepening to determine the optimal number of generations in CSAEA and show that its performance is robust with respect to changes in population size. To test the performance of the procedures developed, we apply them to solve some continuous and mixed-integer nonlinear programming (NLP) benchmarks and show that they obtain better results than those of existing algorithms. [ABSTRACT FROM AUTHOR]

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as the branch and bound method, expert systems, neural networks, and fuzzy reasoning. Recently, modern heuristic methods such as genetic algorithms (GA), simulated annealing (SA), and tabu search (TS) have been attracting notice as efficient methods for solving large combinatorial optimization problems. Moreover, reactive tabu search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search methods for service restoration in distribution systems. This paper develops an RTS for service restoration and compares RTS, GA, and PSA (parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results. © 2000 Scripta Technica, Electr Eng Jpn, 133(3): 71–82, 2000 [ABSTRACT FROM AUTHOR]

The equilibrium network design problem can be formulated as a mathematical program with variational inequality constraints. We know this problem is nonconvex; hence, it is difficult to solve for a globally optimal solution. In this paper we propose a simulated annealing algorithm for the equilibrium network design problem. We demonstrate the ability of this algorithm to determine a globally optimal solution for two different networks. One of these describes an actual city in the midwestern United States. [ABSTRACT FROM AUTHOR]