Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

Construction Time-cost Trade-off Analysis Using Ant System

Abstract: During the last decade, evolutionary methods such as genetic algorithms have been used extensively for solving time-cost trade-off (TCT) problems in construction projects. More recently, Ant systems (AS), which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to a number of combinatorial optimization problems. In this article, a formulation is developed that enables AS to be used for the TCT analysis. With its robust optimization search, the AS model minimizes the total project cost as an objective function and accounts for project-specific constraints on time and cost. The findings of this study indicate that AS algorithms are an attractive alternative to meta-heuristic optimization algorithms in the construction engineering and management domain in terms of computational efficiency and their ability to find near-global optimal solutions.

Five-elements cycle optimization algorithm for the travelling salesman problem

Abstract: There are many nature-inspired algorithms being proposed and researched to solve combinatorial optimization problems. The theory of Five-elements in Chinese traditional culture implies a possible approach to solve present-day problems of science and engineering. In this paper, the Five-elements Cycle Model (FECM) is developed based on the mechanism of generation and restriction among five elements, and Five-elements Cycle Optimization (FECO) algorithm is proposed for finding the optimal solution of travelling salesman problems. The performance and parameter comparison of FECO is given by experiments, the comparison with 7 optimization algorithms based on various mechanisms for some TSP instances from TSPLIB are also given, which indicate the availability of FECO.

Hybrid Approach Based on Ant Algorithm for Solving Traveling Salesman Problem

Abstract: In the present paper the authors introduce an ant algorithm, a distributed algorithm for the solution of combinatorial optimization problems which has been inspired by the observation of real colonies of ants. Then the authors apply a hybrid approach of ant algorithm with 3opt and crossremoving to the traveling salesman problem (TSP). The results show that it is able to find good solutions quickly.

Agent-Based Meta-Heuristic Approach to Discrete Optimization

Abstract: The paper presents an idea of agent-based meta-heuristic integrating a computational optimization system (evolutionary multi-agent system) with ant colony optimization technique. In the proposed model, chosen parameters of ant colonies may be encoded as genotypes and subjected to evolution process carried out by agents. The goal of the whole system is to search for the best solution of the discrete optimization problem based on the results of the ant colonies run using different parameters. The proposed concept forms a base for further research on bringing different interactions known in ant-colony optimization to the inter-agent level. The considerations are illustrated with preliminary experimental results obtained for parallel ant system solving quadratic assignment problem.