abstract: in this paper, optimization of boeing 747 wing has been accomplished for cruise condition (mach number=0.85, flight altitude=35000 ft), where an optimal wing shape is proposed. objective functions are minimization of wing weight and drag force that as well as confining design parameters, two functional constrains are applied. the first functional constrain is fuel tank volume in the a...

This paper investigates fast Pareto genetic algorithm based on fast fitness identification and external population updating scheme (FPGA) for searching Pareto-optimal set, which is based on a new approach of fast fitness identification algorithm for individual and a clustering on the basis of external population updating scheme to maintain population diversity and even distribution of Pareto so...

Patterns are the key building blocks in the logical analysis of data (LAD). It has been observed in empirical studies and practical applications that some patterns are more “suitable” than others for use in LAD. In this paper, we model various such suitability criteria as partial preorders defined on the set of patterns. We introduce three such preferences, and describe patterns which are Paret...

In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in ¯exible manufacturing systems. The PSGA is used to generate approximately ef®cient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the ®rst implementation of PSGA to solve a multiobjective optimization problem (MOP)....

The multi-objective multi-armed bandit (MOMAB) problem is a sequential decision process with stochastic rewards. Each arm generates a vector of rewards instead of a single scalar reward. Moreover, these multiple rewards might be conflicting. The MOMAB-problem has a set of Pareto optimal arms and an agent’s goal is not only to find that set but also to play evenly or fairly the arms in that set....

Pareto local search (PLS) methods are local search algorithms for multiobjective combinatorial optimization problems based on the Pareto dominance criterion. PLS explores the Pareto neighbourhood of a set of non-dominated solutions until it reaches a local optimal Pareto front. In this paper, we discuss and analyse three different Pareto neighbourhood exploration strategies: best, first, and ne...

In this chapter, we discuss the application of evolutionary multiobjective optimization (EMO) to association rule mining. Especially, we focus our attention on classification rule mining in a continuous feature space where the antecedent and consequent parts of each rule are an interval vector and a class label, respectively. First we explain evolutionary multiobjective classification rule mini...

A multi-objective evolutionary algorithm (MOEA) approach is presented in this paper. The algorithm (DFBMOEA) aims to improve convergence of Paretobased MOEAs to the true Pareto optimal set/Pareto front and remove decision maker interaction from the process. A novel distance function is used as a fitness function for MOEA. A range equalisation function and a reference vector are utilised to elim...

In many fields one encounters the challenge of identifying, out of a pool of possible designs, those that simultaneously optimize multiple objectives. This means that usually there is not one optimal design but an entire set of Pareto-optimal ones with optimal tradeoffs in the objectives. In many applications, evaluating one design is expensive; thus, an exhaustive search for the Pareto-optimal...