The use of Pareto-optimal performance fronts in emerging design methodologies for analog integrated circuits is a keystone to overcome the limitations of traditional design methodologies. However, most techniques to generate the fronts reported so far neglect the effect that the surrounding circuitry (such as the load impedance) has on the Pareto-front, thereby making it only realistic for the ...

Multi-objective evolutionary algorithms frequently use an archive of non-dominated solutions to approximate the Pareto front. We show that the truncation of this archive to a limited number of solutions can lead to oscillating and shrinking estimates of the Pareto front. New data structures to permit efficient query and update of the full archive are proposed, and the superior quality of fronta...

We propose a general methodology for approximating the Pareto front of multi-criteria optimization problems. Our search-based methodology consists of submitting queries to a constraint solver. Hence, in addition to a set of solutions, we can guarantee bounds on the distance to the actual Pareto front and use this distance to guide the search. Our implementation, which computes and updates the d...

In this paper, we present PICPA, the “Population and Interval Constraint Propagation Algorithm” which is able to produce high quality approximate solutions while giving guaranteed bounds for the Pareto optimal front. These bounds allow us to know whether the heuristic solutions are close to or far away from the optimal front. PICPA combines “Interval Constraint Propagation” (ICP) techniques [1,...

The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose a new approach in selecting leaders for the particles to follow, which in-turn will guide the algorithm tow...

MULTI-OBJECTIVE OPTIMIZATION OF BLAST SIMULATION USING SURROGATE MODEL Toshihiro Tsuga, M.S. George Mason University, 2007 Thesis Director: Dr. Rainald Löhner A multi objective optimization approach using a Kriging model coupled with a Multi Objective Genetic Algorithm (MOGA) is applied to a blast damage maximization problem composed of two objectives, namely number of casualties and damage to ...

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multiobjective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., nonParallel MOGAs) may fail to solve such intractable problem in a reasonab...

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non-Parallel MOGAs) may fail to solve such intractable problem in a reason...

Finding a best designed experiment based on balancing several competing goodness measures of the design is becoming more important in many applications. The Pareto front approach allows the practitioner to understand trade-offs between alternatives and make more informed decisions. Efficient search for the front is a key to successful use and broad adoption of the method. A substantial computat...

Pareto front optimization has been commonly used for balancing trade-offs between different estimated responses. Using maximum likelihood or least squares point estimates or the worst case confidence bound values of the response surface, it is straightforward to find preferred locations in the input factor space that simultaneously perform well for the various responses. A new approach is propo...