Extremal optimization

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

Extremal optimization for a dye vat scheduling problem

Abstract: This paper models a dyeing scheduling process with sequence dependent changeovers for minimization of costs. To obtain an optimal vat arrangement, we propose an improved extremal optimization (EO) embedded a heuristic rule. In iterations of the EO, each product's fitness is evaluated and a relative bad product is selected and reassigned. This reassignment impacts the related products that are in the same dye vat with the selected one and the related vats that contain the related products, so that all relevant products and vats are reassigned using the heuristic rule. The proposed improved EO exhibits good performance for solving the dye scheduling problem tested by the numerical experiments. The simulations on two groups of data in different size show that the EO gets better dye vat scheduling scheme in shorter time compared with genetic algorithm.

A four-decomposition strategies for hierarchically modeling combinatorial optimization problems: framework, conditions and relations

Abstract: We address the problem of modeling combinatorial optimization problems (COP). COPs are generally complex problems to solve. So a good modeling step is fundamental to make the solution easier. Our approach orients researches to choose the best modeling strategy from the beginning to avoid any problem in the solving process. This paper aims at proposing a new approach dealing with hard COPs particularly when the decomposition process leads to some well-known and canonical optimization sub-problems. We tried to draw a clear framework that will help to model hierarchical optimization problems. The framework will be composed by four decomposition strategies which are: objective based decomposition; constraints based decomposition, semantic decomposition and data partitioning strategy. For each strategy, we present supporting examples from the literature where it was applied. But, not all combinatorial problems can be benefit from the outcomes and benefits of modeling problems hierarchically, rather only particular problems can be modeled like a hierarchical optimization problem. Thus, we propose a set of decomposability conditions for decomposing COPs. Furthermore, we define the types of relationships between obtained sub-problems and how partial solutions can be merged to obtain the final solution.

Network path optimization under dynamic conditions

Abstract: Most network optimization problems are studied under a static scenario in which connectivity of the network and weights associated with the links of the networks are assumed to be fixed. However, in practice, they are likely to change with time and if the network is to be used over time under dynamic conditions, they need to be re-optimized as soon as there is a change. Since optimization process requires some finite time, there is a need for a efficient dynamic optimization strategy for solving such problems. In this study, we extend a previously proposed “Frozen-time” algorithm to network optimization by which new and optimized networks can be obtained in a computationally fast manner. We propose three different variations of the optimization strategies and show proof-of-principle simulation results on a 20-node network having 190 different source-destination paths. The results are interesting and suggest a viable further research.

Some Combinatorial Optimization Problems on which Ant Colony Optimization is Invariant

Abstract: Ant colony optimization is a well known metaheuristic which has been object of many studies, both theoretic and applicative. In a recent analysis [1], a particular aspect of its behavior is investigated. More in detail, the topic object of interest is the result achieved by ant algorithms when the cost unit in which instances are expressed changes. Three ant colony optimization algorithms are proved to be invariant to this type of transformation of instances, provided that some conditions are satisfied. In this paper some examples of combinatorial optimization problems to which ant colony optimization can be applied in an invariant fashion are described. Many of the problems typically tackled in the literature may be included in this list.

Satisfactory Solution of Chinese Travelling Salesman Problem Based on Ant Colony Algorithm

Abstract: Ant colony algorithm is a novel simulated evolutionary algorithm based on group cooperation and can be applied to solve hard discrete combinatorial optimization problem. This paper gives a brief introduction on the ant colony algorithm and modifies it to solve the Chinese travelling salesman problem. Result shows that the performance of ant colony algorithm is improved and the present best solution is found. It also points out that the ant colony algorithm still has to be modified in many ways.