Extremal optimization

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

A Comparison of Three Meta-heuristics for a Closed-Loop Layout Problem with Unequal-Sized Facilities

Abstract: This paper presents a novel mathematical model of a closed-loop layout problem with unequal-sized facilities. This problem belongs to a class of combinatorial optimization and NP-hard problems. Obtaining an optimal solution for this complex, large-sized problem in reasonable computational time by using traditional approaches and is extremely difficult. Therefore, we propose three well-known meta-heuristics, namely genetic algorithm (GA), ant colony optimization (ACO), and simulated annealing (SA), to solve the closed-loop layout problem. These algorithms report near-optimal and promising solutions in a short period of time because of their efficiency. The computational results obtained by these algorithms are compared with the results reported by the Lingo 8.0 software package. Finally among our three proposed meta-heuristics, the output of SA is better than other two algorithms and the Lingo.

An Improved Heuristic for the Probabilistic Traveling Salesman Problem with Deadlines Based on GPGPU

Abstract: Stochastic combinatorial optimization problems have received increasing attention in recent years. These problems can be used to obtain more realistic models for real world applications. The drawback is that stochastic combinatorial optimization problems are usually much harder to solve than their non-stochastic counterparts and therefore efficient heuristics for these problems are of great importance. In this paper we focus on the Probabilistic Traveling Salesman Problem with Deadlines, a well-known stochastic vehicle routing problem. This problem can be efficiently solved using a heuristic based on general-purpose computing on graphics processing units. We show how such a heuristic can be further improved to allow a more efficient utilization of the graphics processing unit. We extensively discuss our results and point out how our techniques can be generalized for solving other stochastic combinatorial optimization problems.

Best - Worst Recombination Scheme for Combinatorial Optimization.

Abstract: A safing and arming timer being responsive to sequentially sampled deceleration forces to operate in one of a non-retard or retard mode wherein different non-retard and retard safe separation times may be predetermined and sequential outputs are available for sequential control of two different output functions.

Application of Ant Colony System in Distribution Reconfiguration

Abstract: Distribution network reconfiguration for loss minimization is a complex,largescale combinatorial optimization problem.As a new heuristic searching technique,ant colony algorithm is suitable for solving combinatorial optimization problem.ACO(ant colony optimization) has positive feedback and the ability of distributed computation,and it is easy to combine with the other algorithms and can do greedy heuristic search.After analyzing the distribution network in topology, the distribution network reconfiguration problem is converted into a problem of constructing spanning tree of the graph,which is solved by breaking-cycle-basis method.An improved ant colony optimization algorithm is proposed to solve the reconfiguration problem,which can expand the search extent and avoid search stagnation by selecting the first-branch randomly and canceling the heuristic value of network.Case study on IEEE 69-bus system proves that the proposed algorithm can obtain the global best solution with less computation time and higher probability compared to the conventional methods.

Ant Colony Algorithm and Its Application in Solving the Traveling Salesman Problem

Abstract: With the modernization of the rapid development of science and technology, high technology has been more and more widely applied. Ant colony algorithm is a novel category of bionic meta-heuristic system and parallel computation and positive feedback mechanism are adopted in this algorithm. Ant colony algorithm, which has strong robustness and is easy to combine with other methods in optimization, has wide application in various combined optimization fields, but the basic ant colony algorithm is of slow convergence and easy to stagnation and easily converges to local solutions. many scholars did a lot of effort to improve these weaknesses, but the research still needs improving. This paper expounds the basic principle, model, advantages and disadvantages of ant colony algorithm and the TSP problem, the concrete realization process of ant colony algorithm is put forward in solving traveling salesman problem and the simulation shows that solution is feasible.