Evolutionary Algorithms for

Many real-world problems involve two types of problem difficulty: i) multiple, conflicting objectives and ii) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Paretooptimal solutions concurrently in a single simulation run. However, in spite of this variety, there is a lack of extensive comparative studies in the literature. Therefore, it has remained open up to now

NACST/Seq: A Sequence Design System with

1 Introduction Using the bio-molecules as basic computing or storage media, DNA computing wins the massive parallelism and some useful features such as the self-assembly. However, the chemical characteristics of materials involve some drawbacks in computing process

SSPMO: A scatter tabu search procedure for non-linear multiobjective optimization

Abstract — We describe the development and testing of a metaheuristic procedure, based on the scatter search methodology, for the problem of approximating the efficient frontier of nonlinear multiobjective optimization problems with continuous variables. Recent applications of scatter search have shown its merit as a global optimization technique for single-objective problems. However, the application of scatter search to multiobjective optimization problems has not been fully explored in the literature. We test the proposed procedure on a suite of problems that have been used extensively in multiobjective optimization. Additional tests are performed on instances that are an extension of those considered classic. The tests indicate that our extension of the basic scatter search framework is a viable alternative for multiobjective optimization

Distributed Genetic Algorithms with a New Sharing Approach in

In this paper, a new distributed genetic algorithm for multiobjective optimization problems is proposed. In this approach, the island model is used with a distributed genetic algorithm and an operation of sharing for Pareto-optimum solutions is performed with the total population. In multiobjective optimization problems, the Pareto-optimum solutions should be derived for designers. Because the Paretooptimum solutions are the set of optimum solutions that are in the relationship of trade-off, not only the accuracy but also the diversity of the solutions should be high. The effect of the distributed populations leads to the high accuracy and the sharing effect leads to the high diversity of solutions. These effects are examined and discussed through some numerical examples that have more than three objective functions

GAs for aerodynamic shape design II:

The lecture focuses on multi-objective genetic algorithms with hybrid capabilities, and on their application to multi-criteria design problems. A short introduction to multipoint aerodynamic shape design is given, and the advantages of a multi-objective optimization approach to this problem are outlined. The introduction of basic concepts of multi-objective optimization is followed by the description of a multiple objective genetic algorithm. Some techniques for e#ciency improvement are introduced; in particular, the gradient based technique for hybrid optimization is extended to multi-objective design problems. Application examples are reported related both to single and multi-element airfoil design in high-lift conditions, and to transonic wing design

Keywords: Optical Networks, Virtual Topologies, Evolutionary Algorithms, Team Algorithm and

Designing virtual topologies is necessary to obtain maximum performance of optical networks. In this paper the routing and wavelength assignation (RWA) is computed using an elitist team of multiobjective evolutionary algorithms, which proposes converting the original RWA problem into a problem of traditional routing, thereby modifying the graph that represents the optical network. The Elitist Team Algorithm simultaneously minimizes the total hop count and the total number of wavelengths for a set of given unicast demands. In this way, a set of optimal solutions, known as a Pareto set, is calculated in only one run of the algorithms without a priori restrictions. 1

IS-PAES: A Constraint-Handling Technique Based on

This paper introduces a new constraint-handling method called InvertedShrinkable PAES (IS-PAES), which focuses the search effort of an evolutionary algorithm on specific areas of the feasible region by shrinking the constrained space of single-objective optimization problems. IS-PAES uses an adaptive grid as the original PAES (Pareto Archived Evolution Strategy). However, the adaptive grid of IS-PAES does not have the serious scalability problems of the original PAES. The proposed constraint-handling approach is validated with several examples taken from the standard literature on evolutionary optimization