Hamidreza Eskandari

A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems

Abstract We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA), for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. FastPGA utilizes a new ranking strategy that utilizes more information about Pareto dominance among solutions and niching relations. New genetic operators are employed to enhance the proposed algorithm’s performance in terms of convergence behavior and computational effort as rapid convergence is of utmost concern and highly desired when solving expensive multiobjective optimization problems (MOPs). Computational results for a number of test problems indicate that FastPGA is a promising approach. FastPGA yields similar performance to that of the improved nondominated sorting genetic algorithm (NSGA-II), a widely-accepted benchmark in the MOEA research community. However, FastPGA outperforms NSGA-II when only a small number of solution evaluations are permitted, as would be the case when solving expensive MOPs.

FastPGA: a dynamic population sizing approach for solving expensive multiobjective optimization problems

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA). FastPGA uses a new fitness assignment and ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. A population regulation operator is introduced to dynamically adapt the population size as needed up to a user-specified maximum population size. Computational results for a number of well-known test problems indicate that FastPGA is a promising approach. FastPGA outperforms the improved nondominated sorting genetic algorithm (NSGA-II) within a relatively small number of solution evaluations.

Enhancing the undergraduate industrial engineering curriculum: Defining desired characteristics and emerging topics

Purpose – This paper aims to present the results of an initial research study conducted to identify the desired professional characteristics of an industrial engineer with an undergraduate degree and the emerging topic areas that should be incorporated into the curriculum to prepare industrial engineering (IE) graduates for the future workforce.Design/methodology/approach – A survey was administered to faculty and industry professionals across the USA to describe the desired characteristics and define the important emerging topic areas. The modified three‐round Delphi technique was applied to obtain consensus and ranking of the emerging topics.Findings – The research findings that identify the desired characteristics and the most important emerging topics to be incorporated into the reengineered curriculum discussed in this paper. Statistical analysis of the results indicates some differences in opinions expressed by persons in academic settings and those working in business and industry.Originality/value...

Handling uncertainty in evolutionary multiobjective optimization: SPGA

This paper presents an extension of the previously developed approach to solve multiobjective optimization problems in deterministic environments by incorporating a stochastic Pareto-based solution ranking procedure. The proposed approach, called stochastic Pareto genetic algorithm (SPGA), employs some statistical analysis on the solution dominance in stochastic problem environments to better discriminate among the competing solutions. Preliminary computational results on three published test problems for different levels of noise with SPGA and NSGA-II are discussed.

Multiobjective simulation optimization using an enhanced genetic algorithm

This paper presents an improved genetic algorithm approach, based on new ranking strategy, to conduct multiobjective optimization of simulation modeling problems. This approach integrates a simulation model with stochastic nondomination-based multiobjective optimization technique and genetic algorithms. New genetic operators are introduced to enhance the algorithm performance of finding Pareto optimal solutions and its efficiency in terms of computational effort. An elitism operator is employed to ensure the propagation of the Pareto optimal set, and a dynamic expansion operator to increase the population size. An importation operator is adapted to explore some new regions of the search space. Moreover, new concepts of stochastic and significant dominance are introduced to improve the definition of dominance in stochastic environments.

Handling Uncertainty In The Analytic Hierarchy Process: A Stochastic Approach

This paper describes a methodology for handling the propagation of uncertainty in the analytic hierarchy process (AHP). In real applications, the pairwise comparisons are usually subject to judgmental errors and are inconsistent and conflicting with each other. Therefore, the weight point estimates provided by the eigenvector method are necessarily approximate. This uncertainty associated with subjective judgmental errors may affect the rank order of decision alternatives. A new stochastic approach is presented to capture the uncertain behavior of the global AHP weights. This approach could help decision makers gain insight into how the imprecision in judgment ratios may affect their choice toward the best solution and how the best alternative(s) may be identified with certain confidence. The proposed approach is applied to the example problem introduced by Saaty for the best high school selection to illustrate the concepts introduced in this paper and to prove its usefulness and practicality.