PID2018 Benchmark Challenge: Multi-Objective Stochastic Optimization Algorithm
نویسندگان
چکیده
منابع مشابه
MOEICA: Enhanced multi-objective optimization based on imperialist competitive algorithm
In this paper, a multi-objective enhanced imperialist competitive algorithm (MOEICA) is presented. The main structures of the original ICA are employed while some novel approaches are also developed. Other than the non-dominated sorting and crowding distance methods which are used as the main tools for comparing and ranking solutions, an auxiliary comparison approach called fuzzy possession is ...
متن کاملStochastic Methods for Multi-Objective Optimization
Real-world optimization problems often involve multiple, competing objectives in a highly complex search space. Multi-objective problems distinguish themselves from single-objective problems in that when preference information is absent no optimal solution is clearly defined but rather a set of alternative trade-off solutions exist, which are called the Pareto-optimal front. Generating Pareto-o...
متن کاملMulti-objective Grasshopper Optimization Algorithm based Reconfiguration of Distribution Networks
Network reconfiguration is a nonlinear optimization procedure which calculates a radial structure to optimize the power losses and improve the network reliability index while meeting practical constraints. In this paper, a multi-objective framework is proposed for optimal network reconfiguration with the objective functions of minimization of power losses and improvement of reliability index. T...
متن کاملMULTI-OBJECTIVE OPTIMIZATION OF TIME-COST-SAFETY USING GENETIC ALGORITHM
Safety risk management has a considerable effect on disproportionate injury rate of construction industry, project cost and both labor and public morale. On the other hand time-cost optimization (TCO) may earn a big profit for project stakeholders. This paper has addressed these issues to present a multi-objective optimization model to simultaneously optimize total time, total cost and overall ...
متن کاملGenetic algorithm for multi-objective experimental optimization
A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default pa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2018
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2018.06.113