Showing papers on "Metaheuristic published in 2011"

Differential Evolution: A Survey of the State-of-the-Art

Abstract: Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

A novel clustering approach: Artificial Bee Colony (ABC) algorithm

Abstract: Artificial Bee Colony (ABC) algorithm which is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behavior of a honey bee swarm. Clustering analysis, used in many disciplines and applications, is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this work, ABC is used for data clustering on benchmark problems and the performance of ABC algorithm is compared with Particle Swarm Optimization (PSO) algorithm and other nine classification techniques from the literature. Thirteen of typical test data sets from the UCI Machine Learning Repository are used to demonstrate the results of the techniques. The simulation results indicate that ABC algorithm can efficiently be used for multivariate data clustering.

Constraint-Handling in Nature-Inspired Numerical Optimization: Past, Present and Future

Abstract: In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem. Nowadays, however, there exists a considerable amount of research devoted to design techniques for handling constraints within a nature-inspired algorithm. This paper presents an analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms. From them, the most popular approaches are analyzed in more detail. For each of them, some representative instantiations are further discussed. In the last part of the paper, some of the future trends in the area, which have been only scarcely explored, are briefly discussed and then the conclusions of this paper are presented.

Hybrid metaheuristics in combinatorial optimization: A survey

Abstract: Research in metaheuristics for combinatorial optimization problems has lately experienced a noteworthy shift towards the hybridization of metaheuristics with other techniques for optimization. At the same time, the focus of research has changed from being rather algorithm-oriented to being more problem-oriented. Nowadays the focus is on solving the problem at hand in the best way possible, rather than promoting a certain metaheuristic. This has led to an enormously fruitful cross-fertilization of different areas of optimization. This cross-fertilization is documented by a multitude of powerful hybrid algorithms that were obtained by combining components from several different optimization techniques. Hereby, hybridization is not restricted to the combination of different metaheuristics but includes, for example, the combination of exact algorithms and metaheuristics. In this work we provide a survey of some of the most important lines of hybridization. The literature review is accompanied by the presentation of illustrative examples.

Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm

Abstract: Distributed generation (DG) has been utilized in some electric power networks. Power loss reduction, environmental friendliness, voltage improvement, postponement of system upgrading, and increasing reliability are some advantages of DG-unit application. This paper presents a new optimization approach that employs an artificial bee colony (ABC) algorithm to determine the optimal DG-unit's size, power factor, and location in order to minimize the total system real power loss. The ABC algorithm is a new metaheuristic, population-based optimization technique inspired by the intelligent foraging behavior of the honeybee swarm. To reveal the validity of the ABC algorithm, sample radial distribution feeder systems are examined with different test cases. Furthermore, the results obtained by the proposed ABC algorithm are compared with those attained via other methods. The outcomes verify that the ABC algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimization problems. The ABC algorithm has only two parameters to be tuned. Therefore, the updating of the two parameters towards the most effective values has a higher likelihood of success than in other competing metaheuristic methods.

Biased random-key genetic algorithms for combinatorial optimization

Abstract: Random-key genetic algorithms were introduced by Bean (ORSA J. Comput. 6:154---160, 1994) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a wide class of combinatorial optimization problems. This paper presents a tutorial on the implementation and use of biased random-key genetic algorithms for solving combinatorial optimization problems. Biased random-key genetic algorithms are a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. After introducing the basics of biased random-key genetic algorithms, the paper discusses in some detail implementation issues, illustrating the ease in which sequential and parallel heuristics based on biased random-key genetic algorithms can be developed. A survey of applications that have recently appeared in the literature is also given.

An introduction and survey of estimation of distribution algorithms

Abstract: Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. This explicit use of probabilistic models in optimization offers some significant advantages over other types of metaheuristics. This paper discusses these advantages and outlines many of the different types of EDAs. In addition, some of the most powerful efficiency enhancement techniques applied to EDAs are discussed and some of the key theoretical results relevant to EDAs are outlined.

Meta-Heuristics: Theory and Applications

Abstract: From the Publisher: Meta-heuristics have developed dramatically since their inception in the early 1980's. They have had widespread success in attacking a variety of practical and difficult combinatorial optimization problems. They incorporate concepts based on biological evolution, intelligent problem solving, mathematical and physical sciences, nervous systems, and statistical mechanics. Since the 1980's, a great deal of effort has been invested in the field of combinatorial optimization theory in which heuristic algorithms have become an important area of research and applications. This volume is drawn from the first conference on Meta-Heuristics and contains 41 papers on the state-of-the-art in heuristic theory and applications. The book treats the following meta-heuristics and applications: Genetic Algorithms, Simulated Annealing, Tabu Search, Networks & Graphs, Scheduling and Control, TSP, and Vehicle Routing Problems. It represents research from the fields of Operations Research, Management Science, Artificial Intelligence and Computer Science.

The best-so-far selection in Artificial Bee Colony algorithm

Abstract: The Artificial Bee Colony (ABC) algorithm is inspired by the behavior of honey bees. The algorithm is one of the Swarm Intelligence algorithms explored in recent literature. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, ABC can sometimes be slow to converge. In order to improve the algorithm performance, we present a modified method for solution update of the onlooker bees in this paper. In our method, the best feasible solutions found so far are shared globally among the entire population. Thus, the new candidate solutions are more likely to be close to the current best solution. In other words, we bias the solution direction toward the best-so-far position. Moreover, in each iteration, we adjust the radius of the search for new candidates using a larger radius earlier in the search process and then reduce the radius as the process comes closer to converging. Finally, we use a more robust calculation to determine and compare the quality of alternative solutions. We empirically assess the performance of our proposed method on two sets of problems: numerical benchmark functions and image registration applications. The results demonstrate that the proposed method is able to produce higher quality solutions with faster convergence than either the original ABC or the current state-of-the-art ABC-based algorithm.

On the usefulness of non-gradient approaches in topology optimization

Abstract: Topology optimization is a highly developed tool for structural design and is by now being extensively used in mechanical, automotive and aerospace industries throughout the world. Gradient-based topology optimization algorithms may efficiently solve fine-resolution problems with thousands and up to millions of design variables using a few hundred (finite element) function evaluations (and even less than 50 in some commercial codes). Nevertheless, non-gradient topology optimization approaches that require orders of magnitude more function evaluations for extremely low resolution examples keep appearing in the literature. This forum article discusses the practical and scientific relevance of publishing papers that use immense computational resources for solving simple problems for which there already exist efficient solution techniques.

Reliable logistics networks design with facility disruptions

Abstract: This paper studies a strategic supply chain management problem to design reliable networks that perform as well as possible under normal conditions, while also performing relatively well when disruptions strike. We present a mixed-integer programming model whose objective is to minimize the nominal cost (the cost when no disruptions occur) while reducing the disruption risk using the p -robustness criterion (which bounds the cost in disruption scenarios). We propose a hybrid metaheuristic algorithm that is based on genetic algorithms, local improvement, and the shortest augmenting path method. Numerical tests show that the heuristic greatly outperforms CPLEX in terms of solution speed, while still delivering excellent solution quality. We demonstrate the tradeoff between the nominal cost and system reliability, showing that substantial improvements in reliability are often possible with minimal increases in cost. We also show that our model produces solutions that are less conservative than those generated by common robustness measures.

Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem

Abstract: Efficient and reliable power production is necessary to meet both the profitability of power systems operations and the electricity demand, taking also into account the environmental concerns about the emissions produced by fossil-fuelled power plants. The economic emission load dispatch problem has been defined and applied in order to deal with the optimization of these two conflicting objectives, that is, the minimization of both fuel cost and emission of generating units. This paper introduces and describes a solution to this famous problem using a new metaheuristic nature-inspired algorithm, called firefly algorithm, which was developed by Dr. Xin-She Yang at Cambridge University in 2007. A general formulation of this algorithm is presented together with an analytical mathematical modeling to solve this problem by a single equivalent objective function. The results are compared with those obtained by alternative techniques proposed by the literature in order to show that it is capable of yielding good optimal solutions with proper selection of control parameters.

Artificial Bee Colony algorithm for optimization of truss structures

Abstract: The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, the Artificial Bee Colony algorithm with an adaptive penalty function approach (ABC-AP) is proposed to minimize the weight of truss structures. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. Five truss examples with fixed-geometry and up to 200 elements were studied to verify that the ABC algorithm is an effective optimization algorithm in the creation of an optimal design for truss structures. The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design.

Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

Abstract: Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed: the maximum entropy based artificial bee colony thresholding (MEABCT) method. Four different methods are compared to this proposed method: the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO), the Fast Otsu’s method and the honey bee mating optimization (HBMO). The experimental results demonstrate that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the other four thresholding methods, the segmentation results of using the MEABCT algorithm is the most, however, the computation time by using the MEABCT algorithm is shorter than that of the other four methods.

Truss optimization with dynamic constraints using a particle swarm algorithm

Abstract: In this paper, a structural truss mass optimization on size and shape is performed taking into account frequency constraints. It is well-known that structural optimizations on shape and size are highly non-linear dynamic optimization problems since this mass reduction conflicts with the frequency constraints especially when they are lower bounded. Besides, vibration modes may switch easily due to shape modifications. This paper intends to investigate the use of a particle swarm optimization (PSO) algorithm as an optimization engine in this type of problem. This choice is based on reported well-behavior of such algorithm as global optimizer in other areas of knowledge. Another feature of the algorithm is taken into account for this choice, like the fact that it is not gradient based, but just based on simple objective function evaluation. The algorithm is briefly revised highlighting its most important features. It is presented four examples regarding the optimization of trusses on shape and size with frequency constraints. The examples are widely reported and used in the related literature as benchmarks. The results show that the algorithm performed similar to other methods and even better in some cases.

Exploratory Power of the Harmony Search Algorithm: Analysis and Improvements for Global Numerical Optimization

Abstract: The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a derivative-free real parameter optimization algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper analyzes the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. A simple but very useful modification to the classical HS has been proposed in light of the mathematical analysis undertaken here. A comparison with the most recently published variants of HS and four other state-of-the-art optimization algorithms over 15 unconstrained and five constrained benchmark functions reflects the efficiency of the modified HS in terms of final accuracy, convergence speed, and robustness.

Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation

Abstract: In this paper, the principal components analysis (PCA) is formulated as a continuous optimisation problem. Then, a novel metaheuristic inspired from nature is employed to explore the search space for the optimum solution to the PCA problem. The new metaheuristic is called 'galaxy-based search algorithm' or 'GbSA'. The GbSA imitates the spiral arm of spiral galaxies to search its surrounding. This spiral movement is enhanced by chaos to escape from local optimums. A local search algorithm is also utilised to adjust the solution obtained by the spiral movement of the GbSA. Experimental results demonstrate that the proposed GbSA for the PCA or GbSA-PCA is a promising tool for the PCA estimation.

Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem

Abstract: One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.

Editorial survey: swarm intelligence for data mining

Abstract: This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining. Whereas data mining has been a popular academic topic for decades, swarm intelligence is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. In recent years the swarm intelligence paradigm has received widespread attention in research, mainly as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). These are also the most popular swarm intelligence metaheuristics for data mining. In addition to an overview of these nature inspired computing methodologies, we discuss popular data mining techniques based on these principles and schematically list the main differences in our literature tables. Further, we provide a unifying framework that categorizes the swarm intelligence based data mining algorithms into two approaches: effective search and data organizing. Finally, we list interesting issues for future research, hereby identifying methodological gaps in current research as well as mapping opportunities provided by swarm intelligence to current challenges within data mining research.

Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques

Abstract: In this paper, we present a new method, called the genetic simulated annealing ant colony system with particle swarm optimization techniques, for solving the traveling salesman problem. We also make experiments using the 25 data sets obtained from the TSPLIB (http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) and compare the experimental results of the proposed method with the methods of Angeniol, Vaubois, and Texier (1988), Somhom, Modares, and Enkawa (1997), Masutti and Castro (2009) and Pasti and Castro (2006). The experimental results show that both the average solution and the percentage deviation of the average solution to the best known solution of the proposed method are better than the methods of Angeniol et al. (1988), Somhom et al. (1997), Masutti and Castro (2009) and Pasti and Castro (2006).

Opt4J: a modular framework for meta-heuristic optimization

Abstract: This paper presents a modular framework for meta-heuristic optimization of complex optimization tasks by decomposing them into subtasks that may be designed and developed separately. Since these subtasks are generally correlated, a separate optimization is prohibited and the framework has to be capable of optimizing the subtasks concurrently. For this purpose, a distinction of genetic representation (genotype) and representation of a solution of the optimization problem (phenotype) is imposed. A compositional genotype and appropriate operators enable the separate development and testing of the optimization of subtasks by a strict decoupling. The proposed concept is implemented as open source reference OPT4J [6]. The architecture of this implementation is outlined and design decisions are discussed that enable a maximal decoupling and flexibility. A case study of a complex real-world optimization problem from the automotive domain is introduced. This case study requires the concurrent optimization of several heterogeneous aspects. Exemplary, it is shown how the proposed framework allows to efficiently optimize this complex problem by decomposing it into subtasks that are optimized concurrently.