—In this work, we provide a technique for efficiently exploring the power/performance design space of a parameterized system-on-a-chip (SOC) architecture to find all Pareto-optimal configurations. These Pareto-optimal configurations will represent the range of power and performance tradeoffs that are obtainable by adjusting parameter values for a fixed application that is mapped on the SOC arch...

In the past few years, multi-objective optimization (MOO) algorithms have been extensively applied in several fields including engineering design problems. A major reason is the advancement of evolutionary multi-objective optimization (EMO) algorithms that are able to find a set of non-dominated points spread on the respective Pareto-optimal front in a single simulation. Besides just finding a ...

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill...

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...

The quality of solution sets generated by decomposition-based evolutionary multiobjective optimisation (EMO) algorithms depends heavily on the consistency between a given problem’s Pareto front shape and the specified weights’ distribution. A set of weights distributed uniformly in a simplex often lead to a set of well-distributed solutions on a Pareto front with a simplex-like shape, but may f...

A wind turbine transformer (WTT) is designed using a 3D wound core while the transformer’s total owning cost (TOC) and its inrush current performance realized as the two objective functions in a multi-objective optimization process. Multi-objective genetic algorithm is utilized to derive Pareto optimal solutions. The effects of inrush current improvement on other operating and design parameters...

An original approach to multi-objective optimization is introduced, using a message-passing algorithm to sample the Pareto set, i.e. the set of Pareto-nondominated solutions. Several heuristics are proposed and tested on a simple biobjective 3-SAT problem. The first one is based on a straightforward deformation of the Survey-Propagation (SP) equation to locally encode a Pareto trade-off. A simp...

1 Abstract Multi-objective optimization addresses problems with several design objectives, which are often conflicting, placing different demands on the design variables. In contradiction to traditional optimization methods, which combine all objectives into a single figure of merit, parallel optimization strategies such as evolutionary algorithms allow direct convergence to the Pareto front. T...

This paper proposes a multi-objective model for the economic-statistical design of the variable sample size and sampling interval multivariate exponentially weighted moving average control chart by using double warning lines. The Markov chain approach is used to obtain the statistical properties. We extend the Lorenzen and Vance cost function considering multiple assignable causes and multivari...

Contemporary evolutionary multiobjective optimisation techniques are becoming increasingly focussed on the notions of archiving, explicit diversity maintenance and population-based Pareto ranking to achieve good approximations of the Pareto front. While it is certainly true that these techniques have been effective, they come at a significant complexity cost that ultimately limits their applica...