Showing papers on "Extremal optimization published in 2007"

Size reduction of complex networks preserving modularity

Abstract: The ubiquity of modular structure in real-world complex networks is being the focus of attention in many trials to understand the interplay between network topology and functionality. The best approaches to the identification of modular structure are based on the optimization of a quality function known as modularity. However this optimization is a hard task provided that the computational complexity of the problem is in the NP-hard class. Here we propose an exact method for reducing the size of weighted (directed and undirected) complex networks while maintaining invariant its modularity. This size reduction allows the heuristic algorithms that optimize modularity for a better exploration of the modularity landscape. We compare the modularity obtained in several real complex-networks by using the Extremal Optimization algorithm, before and after the size reduction, showing the improvement obtained. We speculate that the proposed analytical size reduction could be extended to an exact coarse graining of the network in the scope of real-space renormalization.

Ant Colony Optimization for Multi-Objective Optimization Problems

Abstract: We propose in this paper a generic algorithm based on ant colony optimization to solve multi-objective optimization problems. The proposed algorithm is parameterized by the number of ant colonies and the number of pheromone trails. We compare different variants of this algorithm on the multi-objective knapsack problem. We compare also the obtained results with other evolutionary algorithms from the literature.

Using river formation dynamics to design heuristic algorithms

Abstract: Finding the optimal solution to NP-hard problems requires at least exponential time. Thus, heuristic methods are usually applied to obtain acceptable solutions to this kind of problems. In this paper we propose a new type of heuristic algorithms to solve this kind of complex problems. Our algorithm is based on river formation dynamics and provides some advantages over other heuristic methods, like ant colony optimization methods. We present our basic scheme and we illustrate its usefulness applying it to a concrete example: The Traveling Salesman Problem.

On local optima in multiobjective combinatorial optimization problems

Abstract: In this article, local optimality in multiobjective combinatorial optimization is used as a baseline for the design and analysis of two iterative improvement algorithms. Both algorithms search in a neighborhood that is defined on a collection of sets of feasible solutions and their acceptance criterion is based on outperformance relations. Proofs of the soundness and completeness of these algorithms are given.

Scatter search heuristic for least-cost design of water distribution networks

Abstract: The optimization problems of water distribution networks are complex, multi-modal and discrete-variable problems that cannot be easily solved with conventional optimization algorithms. Heuristic algorithms such as genetic algorithms, simulated annealing, tabu search and ant colony optimization have been extensively employed over the last decade. This article proposed an optimization procedure based on the scatter search (SS) framework, which is also a heuristic algorithm, to obtain the least-cost designs of three well-known looped water distribution networks (two-loop, Hanoi and New York networks). The computational results obtained with the three benchmark instances indicate that SS is able to find solutions comparable to those provided by some of the most competitive algorithms published in the literature.

Hybrid Ant Colony Optimization Using Memetic Algorithm for Traveling Salesman Problem

Abstract: Ant colony optimization was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. Although ant colony optimization for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little research is conducted on the optimum configuration strategy for the adjustable parameters in the ant colony optimization, and the performance of ant colony optimization depends on the appropriate setting of parameters which requires both human experience and luck to some extend. Memetic algorithm is a population-based heuristic search approach which can be used to solve combinatorial optimization problem based on cultural evolution. Based on the introduction of these two meta-heuristic algorithms, a novel kind of adjustable parameters configuration strategy based on memetic algorithm is developed in this paper, and the feasibility and effectiveness of this approach are also verified through the famous traveling salesman problem (TSP). This hybrid approach is also valid for other types of combinational optimization problems

Optimization with extremal dynamics for the traveling salesman problem

Abstract: By mapping the optimization problems to physical systems, the paper presents a general-purpose stochastic optimization method with extremal dynamics. It is built up with the traveling salesman problem (TSP) being a typical NP-complete problem. As self-organized critical processes of extremal dynamics, the optimization dynamics successively updates the states of those cities with high energy. Consequently, a near-optimal solution can be quickly obtained through the optimization processes combining the two phases of greedy searching and fluctuated explorations (ergodic walk near the phase transition). The computational results demonstrate that the proposed optimization method may provide much better performance than other optimization techniques developed from statistical physics, such as simulated annealing (SA). Since the proposed fundamental solution is based on the principles and micromechanisms of computational systems, it can provide systematic viewpoints and effective computational methods on a wide spectrum of combinatorial and physical optimization problems.

Integrated radiation optimization: inspired by the gravitational radiation in the curvature of space-time

Abstract: A novel method for evolutionary optimization, called integrated radiation optimization (IRO), is proposed for solving nonlinear multidimensional optimization problems. Many modern optimization techniques explore the search space by sharing information they have found. In this study, the concept of gravitational radiation in Einstein's theory of general relativity is utilized as a fundamental theory for searching optimal solution in the search space. The idea of developing the algorithm and its detailed procedures are introduced. This work applied the proposed IRO to find the minimum value of a static polynomial function, and some applications that are known to be difficult. The preliminary experimental results show that the performance of the proposed IRO is promising, and IRO shows great performance in solving other NP-hard search and optimization problems.

Studies on Extremal Optimization and Its Applications in Solving RealWorld Optimization Problems

Abstract: Recently, a local-search heuristic algorithm called extremal optimization (EO) has been proposed and successfully applied in some NP-hard combinatorial optimization problems. This paper presents an investigation on the fundamentals of EO with its applications in discrete and numerical optimization problems. The EO was originally developed from the fundamental of statistic physics. However, in this study we also explore the mechanism of EO from all three aspects: statistical physics, biological evolution or co-evolution and ecosystem. Furthermore, we introduce our contributions to the applications of EO in solving traveling salesman problem (TSP) and production scheduling, and multi-objective optimization problems with novel perspective in discrete and continuous search spaces, respectively. The simulation results demonstrate the competitive performance with EO optimization solutions due to its extremal dynamics mechanism

The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty

Abstract: The paper introduces ACO/F-Race, an algorithm for tackling combinatorial optimization problems under uncertainty. The algorithm is based on ant colony optimization and on F-Race. The latter is a general method for the comparison of a number of candidates and for the selection of the best one according to a given criterion. Some experimental results on the probabilistic traveling salesman problem are presented. © 2007 by Springer Science+Business Media, LLC.

Spacecraft thermal design with the Generalized Extremal Optimization Algorithm

Abstract: This article describes an application of the Generalized Extremal Optimization (GEO) algorithm to the inverse design of a spacecraft thermal control system. GEO is a recently proposed global search meta-heuristic (Sousa, F.L. and Ramos, F.M., 2002, Function optimization using extremal dynamics. In: Proceedings of the 4th International Conference on Inverse Problems in Engineering (cd-rom), Rio de Janeiro, Brazil.; Sousa, F.L., Ramos, F.M., Paglione, P. and Girardi, R.M., 2003, New stochastic algorithm for design optimization. AIAA Journal, 41(9), 1808–1818.; Sousa, F.L., Ramos, F.M., Galski, R.L. and Muraoka, I., 2005, Chapter III. In: L.N. De Castro and F.J. Von Zuben (Eds) Generalized Extremal Optimization: A New Meta-heuristic Inspired by a Model of Natural Evolution, Accepted for publication in Recent Developments in Biologically Inspired Computing (Hershey, PA: Idea Group Inc.).) based on a model of natural evolution (Bak, P. and Sneppen, K., 1993, Punctuated equilibrium and criticality in a simple m...

A novel quantum ant colony optimization algorithm

Abstract: Ant colony optimization (ACO) is a technique for mainly optimizing the discrete optimization problem. Based on transforming the discrete binary optimization problem as a "best path" problem solved using the ant colony metaphor, a novel quantum ant colony optimization (QACO) algorithm is proposed to tackle it. Different from other ACO algorithms, Q-bit and quantum rotation gate adopted in quantum-inspired evolutionary algorithm (QEA) are introduced into QACO to represent and update the pheromone respectively. Considering the traditional rotation angle updating strategy used in QEA is improper for QACO as their updating mechanisms are different, we propose a new strategy to determine the rotation angle of QACO. The experimental results demonstrate that the proposed QACO is valid and outperforms the discrete binary particle swarm optimization algorithm and QEA in terms of the optimization ability.

An Improved Ant Colony Optimization Algorithm Based on Route Optimization and Its Applications in Travelling Salesman Problem

Abstract: In this paper, we introduce two improvements on ant colony optimization (ACO) algorithm: route optimization and individual variation. The first is an optimized implementation of ACO, by which the running time of ants routing is largely reduced. The results of the simulated experiments show that the improved algorithm not only reduces the number of routing in the ACO but also surpasses existing algorithms in performance in solving large-scale TSP problems. In the second improvement, we introduce individual variation to ACO, by which the ants have different routing strategies. Simulation results show that the speed of convergence of ACO algorithm could be enhanced greatly.

Strategies for accelerating ant colony optimization algorithms on graphical processing units

Abstract: Ant colony optimization (ACO) is being used to solve many combinatorial problems. However, existing implementations fail to solve large instances of problems effectively. In this paper we propose two ACO implementations that use graphical processing units to support the needed computation. We also provide experimental results by solving several instances of the well-known orienteering problem to show their features, emphasizing the good properties that make these implementations extremely competitive versus parallel approaches.

Adaptive Ant Colony Optimization Algorithm Based on Information Entropy: Foundation and Application

Abstract: In order to solve the premature convergence problem of the basic Ant Colony Optimization algorithm, a promising modification based on the information entropy is proposed. The main idea is to evaluate stability of the current space of represented solutions using information entropy, which is then applied to turning of the algorithm's parameters. The path selection and evolutional strategy are controlled by the information entropy self-adaptively. Simulation study and performance comparison with other Ant Colony Optimization algorithms and other meta-heuristics on Traveling Salesman Problem show that the improved algorithm, with high efficiency and robustness, appears self -adaptive and can converge at the global optimum with a high probability. The work proposes a more general approach to evolutionary-adaptive algorithms related to the population's entropy and has significance in theory and practice for solving the combinatorial optimization problems.

A Genetic Algorithm for the Pickup and Delivery Problem: An Application to the Helicopter Offshore Transportation

Abstract: This paper is a result of the application of soft computing technologies to solve the pick up and delivery problem (PDP). In this paper, we consider a practical PDP that is frequently encountered in the real-world logistics operations, such as Helicopter Offshore Crew Transportation of Oil & Gas Company. We consider a typical scenario of relatively large number of participants, about 70 persons and 5 helicopters. Logistics planning turns to be a combinatorial problem, and that makes it very difficult to find reasonable solutions within a short computational time. We present an algorithm based on two optimization techniques, genetic algorithms and heuristic optimization. Our solution is tested on an example with a known optimal solution, and on actual data provided by PEMEX, Mexican Oil Company. Currently, the algorithm is implemented as part of the system for simulation and optimization of offshore logistics called SMART-Logistics and it is at a field-testing phase.

Application of the generalized extremal optimization algorithm to an inverse radiative transfer problem

Abstract: The recently developed generalized extremal optimization (GEO) algorithm is applied for the solution of an inverse problem of radiative properties estimation. A comparison with two other stochastic methods, simulated annealing and genetic algorithms, is also performed, demonstrating that GEO is competitive. From the test case results we could also infer that a hybridization of GEO with gradient-based methods is very promising.

Two-Stage Ant Colony Optimization for Solving the Traveling Salesman Problem

Abstract: In this paper, a multilevel approach of Ant Colony Optimization to solve the Traveling Salesman Problem is introduced. The basic idea is to split the heuristic search performed by ants into two stages; in this case we use both the Ant System and Ant Colony System algorithms. Also, the effect of using local search was analyzed. We have studied the performance of this new algorithm for several Traveling Salesman Problem instances. Experimental results obtained conclude that the Two-Stage approach significantly improves the Ant System and Ant Colony System in terms of the computation time needed.

Multiobjective extremal optimization with applications to engineering design

Abstract: In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization (MOEO), to solve the constrained multiobjective optimization problems (MOPs). The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-II, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems.

A New Ant Evolution Algorithm to Resolve TSP Problem

Abstract: Traveling salesman problem (TSP) is a combinatorial optimization problem. A new ant evolution algorithm to resolve TSP problem is proposed in this paper. Based on the latest achievement of research on actual ants, the algorithm first takes a set of Pareto optimal solution, which is obtained by scout ants using nearest-neighbor search and diffluence strategy, as the initial population. Then the operators of genetic algorithm, including self-adaptive crossover, mutation and inversion which have the strong local search ability, to speed up the procedure of optimization. Consequently, the optimal solution is obtained relatively fast. The experimental results showed that, the algorithm proposed in this paper is characterized by fast convergence, and can achieve better optimization results.

Enhanced differential evolution hybrid scatter search for discrete optimization

Abstract: A hybrid approach of the enhanced differential evolution (EDE) and scatter search (SS), termed HEDE-SS, is presented in order to solve discrete domain optimization problems. This approach is envisioned in order to capture the randomization properties of EDE and the memory adaptation programming (MAP) properties of SS. Two highly demanding problems of quadratic assignment problem (QAP) and traveling salesman problem (TSP) are optimized with this new heuristic approach. The hybrid obtains the optimal results for almost all of the QAP instances, compares very well for symmetric TSP by getting results around 98 per cent to the optimal.

A Novel Improved Quantum Genetic Algorithm for Combinatorial Optimization Problems

Abstract: Based on quantum genetic algorithm(QGA),a novel improved quantum genetic algorithm(NIQGA)to solve combinatorial optimization problem is proposed.To make full use of interference and entanglement characteristics of quantum state,dynamic step length in adjustment of angle of quantum gate,quantum crossover operation and quantum mutation operation are introduced,therefore high efficiency for optimization is achieved.Two typical combinatorial optimization problems—0/1 knapsack problem and route selection problem,are adopted to confirm the performance of NIQGA.Experimental results show that compared with GA and QGA,NIQGA is characterized by fast convergence rate and excellent capability on global optimization,especially better performance for combinatorial optimization problem with less correlation of genes.

Ant Colony Optimization and Local Search based on Exact and Estimated Objective Values for the Probabilistic Traveling Salesman Problem

Abstract: This paper deals with a general choice that one faces when developing an algorithm for a stochastic optimization problem: either design problem-specific algorithms that exploit the exact objective function, or to consider algorithms that only use estimated values of the objective function, which are very general and for which simple non-sophisticated versions can be quite easily designed. The Probabilistic Traveling Salesman Problem and the Ant Colony Optimization metaheuristic are used as a case study for this general issue. We consider four Ant Colony Optimization algorithms with different characteristics. Two algorithms exploit the exact objective function of the problem, and the other two use only estimated values of the objective function by Monte Carlo sampling. For each of these two groups, we consider both hybrid and non-hybrid versions (that is, with and without the application of a local search procedure). Computational experiments show that the hybrid version based on exact objective values outperforms the other variants and other state-of-the-art metaheuristics from the literature. Experimental analysis on a benchmark of instances designed on purpose let us identify in which conditions the performance of estimationbased variants can be competitive with the others.

The Travelling Salesman’s Problem: A self-adapting PSO-ACS algorithm

Abstract: This paper presents a combination of two well-known metaheuristic algorithms, particle swarm optimization (PSO) and ant colony system (ACS), based on a framework design named A-B-Domain. We take the travelling salesmanpsilas problem as the benckmark problem. ACPS2, as we name this combination, works as a metaheuristic for the TSP. When considering deviations to lower bounds, ACPS2 shows an improvement over the simple ACS with a high computational cost. Proposed policies are able to reduce, significatively, running times. As a final conclusion we observe that a guided search through ACS possible sets of parameters obtains better results than the basic ACS with an extended number of trials.

Using Prediction to Improve Combinatorial Optimization Search

Abstract: This paper describes a statistical approach to improving the performance of stochastic search algorithms for optimization. Given a search algorithm A, we learn to predict the outcome of A as a function of state features along a search trajectory. Predictions are made by a function approximator such as global or locally-weighted polynomial regression; training data is collected by Monte-Carlo simulation. Extrapolating from this data produces a new evaluation function which can bias future search trajectories toward better optima. Our implementation of this idea, STAGE, has produced very promising results on two large-scale domains.

Bi-level programming for continuous network design of comprehensive transportation system based on external optimization

Abstract: To determine the optimal investments on road network of comprehensive transportation system, a bi-level programming model for continuous network design problem was employed. At the upper level problem, planner makes investment decision in links and hubs of a comprehensive transportation system to minimize the total times costs plus investment costs and the external costs such as environment pollution, land use and energy exhaustion. At the lower level, users choose their paths in accordance with deterministic user equilibrium. We design an algorithm based on extremal optimization to solve the upper level problem, in which only investments on those links and hubs with high marginal cost are updated randomly at each step to combine the merits of gradient-based methods and intelligent heuristics. Numerical comparison was made on a grid network with 9 nodes and 14 links. The result shows that the algorithm is competitive with gradient-based and simulated annealing methods.