Showing papers on "Extremal optimization published in 2013"

MOEA/D-ACO: A Multiobjective Evolutionary Algorithm Using Decomposition and AntColony

Abstract: Combining ant colony optimization (ACO) and the multiobjective evolutionary algorithm (EA) based on decomposition (MOEA/D), this paper proposes a multiobjective EA, i.e., MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single-objective optimization problems. Each ant (i.e., agent) is responsible for solving one subproblem. All the ants are divided into a few groups, and each ant has several neighboring ants. An ant group maintains a pheromone matrix, and an individual ant has a heuristic information matrix. During the search, each ant also records the best solution found so far for its subproblem. To construct a new solution, an ant combines information from its group's pheromone matrix, its own heuristic information matrix, and its current solution. An ant checks the new solutions constructed by itself and its neighbors, and updates its current solution if it has found a better one in terms of its own objective. Extensive experiments have been conducted in this paper to study and compare MOEA/D-ACO with other algorithms on two sets of test problems. On the multiobjective 0-1 knapsack problem, MOEA/D-ACO outperforms the MOEA/D with conventional genetic operators and local search on all the nine test instances. We also demonstrate that the heuristic information matrices in MOEA/D-ACO are crucial to the good performance of MOEA/D-ACO for the knapsack problem. On the biobjective traveling salesman problem, MOEA/D-ACO performs much better than the BicriterionAnt on all the 12 test instances. We also evaluate the effects of grouping, neighborhood, and the location information of current solutions on the performance of MOEA/D-ACO. The work in this paper shows that reactive search optimization scheme, i.e., the “learning while optimizing” principle, is effective in improving multiobjective optimization algorithms.

Phase transitions in combinatorial optimization problems

Abstract: Phase transitions in combinatorial optimization problems , Phase transitions in combinatorial optimization problems , کتابخانه دیجیتال جندی شاپور اهواز

Hybrid Mean-Variance Mapping Optimization for solving the IEEE-CEC 2013 competition problems

Abstract: Mean-Variance Mapping Optimization (MVMO) is a recent addition to the emerging field of heuristic optimization algorithms, which has been quite successful in solving a variety of power system optimization problems. This paper introduces a hybrid variant of MVMO (MVMO-SH) for solving the IEEECEC 2013 competition test suite. MVMO-SH is based on a swarm scheme of MVMO with embedded local search and multi-parent crossover strategies to increase search diversity and solution quality. Numerical results attest to the promising prospect of MVMO-SH to become a general purpose optimization algorithm.

The travelling salesman problem with time windows: Adapting algorithms from travel-time to makespan optimization

Abstract: In combinatorial optimization it is not rare to find problems whose mathematical structure is nearly the same, differing only in some aspect related to the motivating application. For example, many problems in machine scheduling and vehicle routing have equivalent formulations and only differ with respect to the optimization objective, or particular constraints. Moreover, while some problems receive a lot of attention from the research community, their close relatives receive hardly any attention at all. Given two closely related problems, it is intuitive that it may be effective to adapt state-of-the-art algorithms-initially introduced for the well-studied problem variant-to the less-studied problem variant. In this paper we provide an example based on the travelling salesman problem with time windows that supports this intuition. In this context, the well-studied problem variant minimizes the travel time, while the less-studied problem variant minimizes the makespan. Indeed, the results show that the algorithms that we adapt from travel-time minimization to makespan minimization significantly outperform the existing state-of-the-art approaches for makespan minimization.

Binary ant colony optimization applied to variable screening in the Mahalanobis-Taguchi System

Abstract: This work presents the application of the Mahalanobis-Taguchi System (MTS) to a dimensional problem in the automotive industry. The combinatorial optimization problem of variable selection is solved by the application of a recent version of binary ant colony optimization algorithm. Moreover, a comparison with respect to binary particle swarm optimization algorithm is also presented and a discussion regarding the numerical results is given.

Clustering social networks using ant colony optimization

Abstract: Several e-marketing applications rely on the ability to understand the structure of social networks. Social networks can be represented as graphs with customers as nodes and their interactions as edges. Most real world social networks are known to contain extremely dense subgraphs (also called as communities) which often provide critical insights about the emergent properties of the social network. The communities, in most cases, correspond to the various segments in a social system. Such an observation led researchers to propose algorithms to detect communities in networks. A modularity measure representing the quality of a network division has been proposed which on maximization yields good partitions. The modularity maximization is a strongly NP-complete problem which renders mathematical programming based optimization intractable for large problem sizes. Many heuristics based on simulated annealing, genetic algorithms and extremal optimization have been used to maximize modularity but have lead to suboptimal solutions. In this paper, we propose an ant colony optimization (ACO) based approach to detect communities. To the best of our knowledge, this is the first application of ACO to community detection. We demonstrate that ACO based approach results in a significant improvement in modularity values as compared to existing heuristics in the literature. The reasons for this improvement when tested on real and synthetic data sets are discussed.

Ant Colony Optimization for task allocation in Multi-Agent Systems

Abstract: Task allocation is a key issue of agent cooperation mechanism in Multi-Agent Systems. The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogeneity impose new challenges on the task allocation in Multi-Agent environments. Based on the traditional parallel computing task allocation method and Ant Colony Optimization (ACO), a novel task allocation method named Collection Path Ant Colony Optimization (CPACO) is proposed to achieve global optimization and reduce processing time. The existing problems of ACO are analyzed; CPACO overcomes such problems by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a three-dimensional path pheromone storage space. The experimental results show that CPACO consumed only 10.3% of the time taken by the Global Search Algorithm and exhibited better performance than the Forward Optimal Heuristic Algorithm.

A Method to Solve the Traveling Salesman Problem Using Ant Colony Optimization Variants with Ant Set Partitioning

Abstract: In this paper we propose an ant’s partition method for Ant Colony Optimization (ACO), a meta-heuristic that is inspired in ant’s behavior and how they collect their food. The proposed method equivalently divides the total number of ants in three different subsets and each one is evaluated separately by the corresponding variation of ACO (AS, EAS, MMAS) to solve different instances of The Traveling Salesman Problem (TSP). This method is based on the idea of “divide and conquer” to be applied in the division of the work, as the ants are evaluated in different ways in the same iteration. This method also includes a stagnation mechanism that stops at a certain variation if it’s not working properly after several iterations. This allows us to save time performing tests and have less overhead in comparison with the conventional method, which uses just one variation of ACO in all iterations.

Analysis of the suitability of using blind crossover operators in genetic algorithms for solving routing problems

Abstract: Genetic algorithms (GA) are one of the most successful techniques in solving combinatorial optimization problems. Its general character has enabled its application to different types of problems: vehicle routing, planning, scheduling, etc. This article shows that there is controversy in the basic structure of the algorithm steps when it is applied at routing problems. Specifically in this paper we show that the crossover (CX) offers no advantage in the optimization process. To solve such problems, the most important steps are mutation and selection of individuals. These two steps are what help to analyze the solution space exhaustively and give GA optimization capability. To prove our hypothesis we will analyze the results obtained by applying different blind crossover operators to solve multiple instances of the TSP (Travelling Salesman Problem).

Solving Travelling Salesman Problem Using Genetic Algorithm

Abstract: Travelling salesman problem (TSP) is a combinatorial optimization problem. It is NP hard problem and TSP is the most intensively studied problem in the area of optimization. But with the increase in the number of cities, the complexity of the problem goes on increasing. In this paper, we have solved Travelling Salesman Problem using Genetic algorithm approach. System starts from a matrix of the calculated Euclidean distances between the cities to be visited by the travelling salesman and randomly chosen city order as the initial population. Then new generations are created repeatedly until the proper path is reached upon reaching a stopping criterion. Keywords-TSP, GA, Fitness value, Selection, 2-point Crossover, Interchange Mutation.

Modified ant colony optimization algorithm with uniform mutation using self-adaptive approach for travelling salesman problem

Abstract: Ant Colony Optimization (ACO) algorithm is a novel meta-heuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. It has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient modified ant colony optimization algorithm with uniform mutation using self-adaptive approach for the travelling salesman problem (TSP) has been proposed. Here mutation operator is used for enhancing the algorithm escape from local optima. The algorithm converges to the final optimal solution, by accumulating most effective sub-solutions. Experimental results show that the proposed algorithm is better than the algorithm previously proposed.

Hybridization of Fuzzy PSO and Fuzzy ACO applied to TSP

Abstract: Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms have attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving real world optimization problems. Swarm-inspired optimization has recently become very popular. Both ACO and PSO are successfully applied in the Traveling Salesman Problem (TSP). Our approach consists in combining Fuzzy Logic with ACO (FACO - Fuzzy Ant Colony Optimization) and PSO (FPSO - Fuzzy Particle Swarm Optimization) for solving the TSP. Experimental results and comparative studies illustrate the importance of Fuzzy logic in reducing the time and the best length for the TSP problems considered.

A constrained solid tsp in fuzzy environment: two heuristic approaches

Abstract: A solid travelling salesman problem (STSP) is a travelling sales- man problem (TSP) where the salesman visits all the cities only once in his tour using dierent conveyances to travel from one city to another. Costs and environmental eect

The Improvement of Fruit Fly Optimization Algorithm-Using Bivariable Function as Example

Abstract: Optimization problems are always the hot issues in various research fields. The aim of this paper is to find the optimal value of the bivariable nonlinear function by means of the improved fruit fly optimization algorithm (G-FOA). Some better results are obtained. Compared with other algorithms, G-FOA is concise, can quickly find the global optimum with the high accuracy and without falling into local extremum. These advantages make the algorithm has good robustness and applicability.

Load balancing in distributed applications based on extremal optimization

Abstract: The paper shows how to use Extremal Optimization in load balancing of distributed applications executed in clusters of multicore processors interconnected by a message passing network. Composed of iterative optimization phases which improve program task placement on processors, the proposed load balancing method discovers dynamically the candidates for migration with the use of an Extremal Optimization algorithm and a special quality model which takes into account the computation and communication parameters of the constituent parallel tasks. Assessed by experiments with simulated load balancing of distributed program graphs, a comparison of the proposed Extremal Optimization approach against a deterministic approach based on a similar load balancing theoretical model is provided.

Modified Ant Colony Optimization Algorithm with Uniform Mutation using Self-Adaptive Approach

Abstract: Colony Optimization (ACO) algorithm is a novel meta- heuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. Ant Colony Optimization has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient ant colony optimization algorithm with uniform mutation operator using self-adaptive approach has been proposed. Here mutation operator is used for enhancing the algorithm escape from local optima. The algorithm converges to the optimal final solution, by gathering the most effective sub-solutions. Experimental results show that the proposed algorithm is better than the algorithm previously proposed.

An Ant Colony Optimization Approach for Nurse Rostering Problem

Abstract: Nurse rostering is a non-deterministic polynomial problem with many constraints. In the literature, a number of heuristic approaches have been proposed, but few of them can achieve satisfying performance on both solution quality and search speed. Inspired by the successful experience of ant colony optimization (ACO) on many highly-constrained problems, this paper proposed an ant colony optimization approach termed ACO-NR for solving the nurse rostering problem. First, the search space of the nurse rostering problem is remodeled as a graph, with each solution corresponding to a path on the graph. Then a heuristic function is designed to guide the path construction behavior of ACO-NR. The heuristic information comes not only from the static information defined by the problem-dependent knowledge, but also from the dynamic information generated by the solution construction procedure. A penalty function is defined to help ACO-NR handle problem constraints. Experimental results on 52 benchmark instances show that the proposed ACO-NR can achieve better performance than classic nurse rostering algorithms.

A Hybrid Algorithm for Community Detection Using PSO and EO

Abstract: Community detection in networks is one of the most prominent areas of network science which is very hard and not yet satisfactorily solved. A hybrid algorithm based on particle swarm optimization (PSO) and Extremal Optimization (EO) for community detection is. PSO algorithm has strong global search ability but is easily to trap into the local optima, while EO algorithm can make the search to jump out of local optima due to its strong local search ability. A special encoding scheme based on the partition solution of a network is designed which can automatically determine the number of the community in a network. The popular modularity Q is used as the fitness of PSO algorithm and node betweenness centrality is adopted as the fitness of EO algorithm. EO algorithm can repair the isolated nodes existing in the particles and make the partition result more precise. Several experiments in real networks demonstrate that the algorithm obtains high modularity and achieves good community results.

Multi-Phase Meta-Heuristic for Multi-Depots Vehicle Routing Problem

Abstract: In this work, we present a multi-phase hybrid algorithm based on clustering to solve the multi-depots vehicle routing problem (MDVRP). The proposed algorithm initially adopts K-means algorithm to execute the clustering analyses, which take the depots as the centroids of the clusters, for the all customers of MDVRP, then implements the local depth search using the Shuffled Frog Leaping Algorithm (SFLA) for every cluster, and then globally re-adjusts the solutions, i.e., rectifies positions of all frogs by the extremal optimization (EO). The processes will continue until the convergence criterions are satisfied. The results of experiments have shown that the proposed algorithm possesses outstanding performance to solve the MDVRP.

Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Travelling Salesman Problem Benchmark Generator

Abstract: The field of dynamic optimization is related to the applications of nature-inspired algorithms [1]. The area is rapidly growing on strategies to enhance the performance of algorithms, but still there is limited theoretical work, due to the complexity of natureinspired algorithms and the difficulty to analyze them in the dynamic domain. Therefore, the development of BGs to evaluate the algorithms in dynamic optimization problems (DOPs) is appreciated by the evolutionary computation community. Such tools are not only useful to evaluate algorithms but also essential for the development of new algorithms. The exclusive-or (XOR) DOP generator [5] is the only general benchmark for the combinatorial space that constructs a dynamic environment from any static binaryencoded function f(x(t)), where x(t) ∈ {0, 1}, by a bitwise XOR operator. XOR DOP shifts the population of individuals into a different location in the fitness landscape. Hence, the global optimum is known during the environmental changes. In the case of permutation-encoded problems, e.g., the travelling salesman problem (TSP) where x(t) is a set of numbers that represent a position in a sequence, the BGs used change the fitness landscape. For example, the dynamic TSP (DTSP) with exchangeable cities [2]

New Bio-inspired Meta-Heuristics - Green Herons Optimization Algorithm - for Optimization of Travelling Salesman Problem and Road Network

Abstract: Following the nature and its processes has been proved to be very fruitful when it comes to tackling the difficult hardships and making life easy Yet again the nature and its processes has been proven to be worthy of following, but this time the discrete family is being facilitated and another member is added to the bio-inspired computing family A new biological phenomenon following meta-heuristics called Green Heron Optimization Algorithm (GHOA) is being introduced for the first time which acquired its potential and habit from an intelligent bird called Green Heron whose diligence, skills, perception analysis capability and procedure for food acquisition has overwhelmed many zoologists This natural skillset of the bird has been transferred into operations which readily favor the graph based and discrete combinatorial optimization problems, both unconstrained and constraint though the latter requires safe guard and validation check so that the generated solutions are acceptable With proper modifications and modeling it can also be utilized for other wide variety of real world problems and can even optimize benchmark equations In this work we have mainly concentrated on the algorithm introduction with establishment, illustration with minute details of the steps and performance validation of the algorithm for a wide range of dimensions of the Travelling Salesman Problem combinatorial optimization problem datasets to clearly validate its scalability performance and also on a road network for optimized graph based path planning The result of the simulation clearly stated its capability for combination generation through randomization and converging global optimization and thus has contributed another important member of the bio-inspired computation family

Simple probabilistic population based optimization for combinatorial optimization

Abstract: A new scheme is proposed for the design of probabilistic population based optimization algorithms for solving combinatorial optimization problems. The new scheme, Simple Probabilistic Population Based Optimization scheme (SPPBO), is used also to classify existing metaheuristics, e.g., the Population-based Ant Colony Optimization algorithm (PACO) and the Simplified Swarm Optimization algorithm (SSO). The classification shows the close relationship between PACO and SSO. This fact has not been recognized in the literature so far. SPPBO is also used to identify new metaheuristics that come up naturally as variants and combinations of PACO and SSO. An experimental study is done to evaluate and compare the different algorithms when applied to the Traveling Salesperson Problem. The results show which parts of the algorithms are helpful for obtaining a good optimization behaviour. In addition to the original PACO and SSO algorithms also some of the new combinations perform very well.

A New Algorithm Based on Differential Evolution for Combinatorial Optimization

Abstract: Differential evolution (DE) was originally designed to solve continuous optimization problems, but recent works have been investigating this algorithm for tackling combinatorial optimization (CO), particularly in permutation-based combinatorial problems. However, most DE approaches for combinatorial optimization are not general approaches to CO, being exclusive for per mutational problems and often failing to retain the good features of the original continuous DE. In this work we introduce a new DE-based technique for combinatorial optimization to addresses these issues. The proposed method employs operations on sets instead of the classical arithmetic operations, with the DE generating smaller sub problems to be solved. This new approach can be applied to general CO problems, not only permutation-based ones. We present results on instances of the traveling salesman problem to illustrate the adequacy of the proposed algorithm, and compare it with existing approaches.

Protein Structure Optimization with a "Lamarckian" Ant Colony Algorithm

Abstract: We describe the LamarckiAnt algorithm: a search algorithm that combines the features of a "Lamarckian” genetic algorithm and ant colony optimization. We have implemented this algorithm for the optimization of BLN model proteins, which have frustrated energy landscapes and represent a challenge for global optimization algorithms. We demonstrate that LamarckiAnt performs competitively with other state-of-the-art optimization algorithms.

A New Hybrid Metaheuristic – Combining Stochastic Tunneling and Energy Landscape Paving

Abstract: (Hybrid) metaheuristics such as simulated annealing, genetic algorithms, or extremal optimization play a most prominent role in global optimization. The performance of these algorithms and their respective sampling behavior during the search process are themselves interesting problems. Here, we show that a combination of two approaches – namely Energy Landscape Paving (ELP) and Stochastic Tunneling (STUN) – can overcome known problems of other Metropolis-sampling-based procedures. We show on grounds of non-equilibrium statistical mechanics and empirical evidence on the synergistic advantages of this combined approach and discuss simulations for a complex optimization problem.

Improved ant colony optimization based on particle swarm optimization and its application

Abstract: This article introduces a novel algorithm to solve the large time-consuming problem of the existing improved ant colony optimization(ACO) based on particle swarm optimization(PSO).A new pheromone update method which combines the global asynchronous feature and elitist strategy was used in the algorithm.Moreover,the iteration steps of ACO invoked by PSO were reasonably reduced.The algorithm was applied to solve the path planning problem of landfill inspection robots in Asahikawa,Japan.It is shown that the algorithm has a better performance in search speed compared with other algorithms recently reported.