Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
Papers published on a yearly basis
Papers
More filters
01 Jan 2005
TL;DR: A heuristic algorithm to obtain such a better solution of the sum-of-ratios problem by means of Ant Colony System is developed and can be used for designing a globally optimal algorithm with the help of some certain strategy of global search.
Abstract: Many applications arising from areas of economics, finance and engineering are cast into the sum-of-ratios problem. Usually, the problems are on such a large scale that the existing algorithms are naive yet to obtain an optimal solution of the problems. In this study we develop a heuristic algorithm to obtain such a better solution of the sum-of-ratios problem by means of Ant Colony System. The proposed algorithm can be used for designing a globally optimal algorithm with the help of some certain strategy of global search as well. We report numerical experiments of the heuristic algorithm, which indicates that the best function value obtained from our heuristic algorithm is empirically near to the optimal value with a high probability.
15 Dec 2022
TL;DR: In this article , the authors proposed an ant colony optimization approach with the traveling salesman problem (ACO-TSP) for DNA sequence optimization, which is more reliable and generates higher quality results.
Abstract: The Ant Colony Optimization Algorithm is a novel optimization algorithm based on the intelligence of ant behavior, whereas the Traveling Salesman Problem is the problem of determining the shortest route between a group of cities that start in one city and visit each other city only once before returning to the starting (home) city. This study proposes an Ant Colony Optimization approach with the Traveling Salesman Problem (ACO-TSP) for DNA Sequence Optimizations. The proposed technique is a unique ant colony optimization approach for reconstructing DNA sequences from fragments of DNA. Existing meta-heuristics, on the other hand, are consistently outperformed in terms of performance by newly invented constructive heuristics. This model was developed based on these novel heuristics, with four nodes (cities) representing the four DNA bases. According to the findings of the experiments, the new approach is more reliable and generates higher-quality results.
Journal Article•
TL;DR: Since this optimization method considers the micro-mechanisms of computational systems, it provides a systematic viewpoint on computational complexity and effectively helps the design of optimization dynamics on a wide spectrum of combinatorial optimization problems.
Abstract: Traveling salesman problem(TSP) has wide applications on optimization theory and engineering practiceWith the definition of discrete state variables and local fitness,we analyze the microscopic characteristics of TSP solutions and present a novel self-organized optimization algorithm with extremal dynamicsIn this algorithm,the local optimal solutions can be effectively found by the optimization dynamics combining greedy search with fluctuated explorationsComputational results on typical TSP benchmark problems in TSPLIB demonstrate that the proposed algorithm outperforms competing optimization techniques,such as simulated annealing(SA) and genetic algorithm(GA)Since this optimization method considers the micro-mechanisms of computational systems,it provides a systematic viewpoint on computational complexity and effectively helps the design of optimization dynamics on a wide spectrum of combinatorial optimization problems
TL;DR: In this article, an exact method for reducing the size of weighted (directed and undirected) complex networks while maintaining invariant its modularity is proposed, which allows the heuristic algorithms that optimize modularity for a better exploration of the modularity landscape.
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.
28 Mar 1993
TL;DR: A new approach to combinatorial optimization problems, called the single minimum method (SMM), using the analogy of thermodynamics is proposed, and an algorithm based on it is suggested for solving the traveling salesman problem.
Abstract: The problem of local minima often appears when solving combinatorial optimization problems by conventional methods relying on the minimization of an objective function. A new approach to combinatorial optimization problems, called the single minimum method (SMM) is proposed. An analysis using the analogy of thermodynamics is given. In order to show how the method works, an algorithm based on it is suggested for solving the traveling salesman problem. The simulation results show that, for 10-city problems, the algorithm can find the shortest or near shortest path with a high success rate. >