We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-...

This paper proposes a multi-objective memetic algorithm based on NSGA-II and Simulated Annealing (SA), NSGA-II-SA, for calibration of microscopic vehicular traffic flow simulation models. The NSGA-II algorithm performs a scan in the search space and obtains the Pareto front which is optimized locally with SA. The best solution of the obtained front is selected. Two CORSIM models were calibrated...

Most contemporary multi-objective evolutionary algorithms (MOEAs) store and handle a population with a linear list, and this may impose high computational complexities on the comparisons of solutions and the fitness assignment processes. This paper presents a data structure for storing the whole population and their dominating information in MOEAs. This structure, called a Dominance Tree (DT), ...

The nondominated sorting genetic algorithm with elitism (NSGA-II) is widely used due to its good performance on solving multiobjective optimization problems. In each iteration of NSGA-II, truncation selection is performed based on the rank and crowding distance of each solution. There are, however, drawbacks in this process. These drawbacks to some extent cause overlapping solutions in the popu...

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valvepoint loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED prob...

To solve single and multi-objective optimization problems, evolutionary algorithms have been created. We use the non-dominated sorting genetic algorithm (NSGA-II) to find Pareto front in a two-objective portfolio query, its extended variant NSGA-III three-objective problem, this article. Furthermore, both we quantify Karush-Kuhn-Tucker Proximity Measure (KKTPM) for each generation determine how...

اثرات ویرانگر بلایای طبیعی، اهمیت لجستیک و برنامه‌ریزی منابع انسانی را در مراحل قبل بعد از بحران نشان می‌دهد. هنگام بروز به ‌منظور امدادرسانی سریع، شبکه سلسله ‌مراتبی سلامت که شامل درمانگاه‌ها بیمارستان‌ها است، فعال می‌شود. این پژوهش با استفاده مدل ریاضی مختلط عدد صحیح درنظرگرفتن موقعیت فعلی درمانگاه‌ها، مکان‌های بهینه‌ای عنوان «مراکز درمان موقت» تعیین نحوه تخصیص بهینه مصدومان ناحیه‌های شهری مر...