Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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22 Jun 2007
TL;DR: In this paper, the authors consider the Ant Colony Optimization (ACO) metaheuristic for stochastic optimization problems and show that the hybrid version based on exact objective values outperforms the other variants and other state-of-the-art metaheuristics.
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.
16 citations
TL;DR: The algorithms ψ- MAXMEAN ∗ are shown to be best-possible in the class of polynomial algorithms (if P ≠ NP ), in both absolute and relative terms.
16 citations
TL;DR: Using a simple, annealed model, some of the key features of the recently introduced extremal optimization heuristic are demonstrated and it is shown that the dynamics of local search possesses a generic critical point under the variation of its sole parameter.
Abstract: Using a simple, annealed model, some of the key features of the recently introduced extremal optimization heuristic are demonstrated. In particular, it is shown that the dynamics of local search possesses a generic critical point under the variation of its sole parameter, separating phases of too greedy (non-ergodic, jammed) and too random (ergodic) exploration. Comparison of various local search methods within this model suggests that the existence of the critical point is essential for the optimal performance of the heuristic.
16 citations
12 Jul 2008
TL;DR: This paper covers a multi-objective Ant Colony Optimization, which is applied to the NP-complete multi-Objective shortest path problem in order to approximate Pareto-fronts.
Abstract: This paper covers a multi-objective Ant Colony Optimization, which is applied to the NP-complete multi-objective shortest path problem in order to approximate Pareto-fronts. The efficient single-objective solvability of the problem is used to improve the results of the ant algorithm significantly. A dynamic program is developed which generates local heuristic values on the edges of the problem graph. These heuristic values are used by the artificial ants.
16 citations
05 Jun 2011
TL;DR: This work evaluated a new immune-inspired algorithm, named cob-aiNet, which adopted a novel concentration-based model of immune network, and its overall optimization performance, based on four Traveling Salesman Problems.
Abstract: Diversity maintenance is an important aspect in population-based metaheuristics for optimization, as it tends to allow a better exploration of the search space, thus reducing the susceptibility to local optima in multimodal optimization problems. In this context, metaheuristics based on the Artificial Immune System (AIS) framework, especially those inspired by the Immune Network theory, are known to be capable of stimulating the generation of diverse sets of solutions for a given problem, even though generally implementing very simple mechanisms to control the dynamics of the network. To increase such diversity maintenance capability even further, a new immune-inspired algorithm was recently proposed, which adopted a novel concentration-based model of immune network. This new algorithm, named cob-aiNet (Concentration-based Artificial Immune Network), was originally developed to solve real-parameter single-objective optimization problems, and it was later extended (with cob-aiNet[MO]) to deal with real-parameter multi-objective optimization. Given that both cob-aiNet and cob-aiNet[MO] obtained competitive results when compared to state-of-the-art algorithms for continuous optimization and also presented significantly improved diversity maintenance mechanisms, in this work the same concentration-based paradigm was further explored, in an extension of such algorithms to deal with single-objective combinatorial optimization problems. This new algorithm, named cob-aiNet[C], was evaluated here in a series of experiments based on four Traveling Salesman Problems (TSPs), in which it was verified not only the diversity maintenance capabilities of the algorithm, but also its overall optimization performance.
16 citations