for multi-objective optimal reactive power dispatch (morpd), a new approach is proposed where simultaneous minimization of the active power transmission loss, the bus voltage deviation and the voltage stability index of a power system are achieved. optimal settings of continuous and discrete control variables (e.g. generator voltages, tap positions of tap changing transformers and the number of...

this paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. this paper presen...

Ant Colony Optimization (ACO) algorithm has evolved as the most popular way to attack the combinatorial problems. The ACO algorithm employs multi agents called ants that are capable of finding optimal solution for a given problem instances. These ants at each step of the computation make probabilistic choices to include good solution component in partially 1 / 4

Ant Colony Optimization (ACO) algorithm has evolved as the most popular way to attack the combinatorial problems. The ACO algorithm employs multi agents called ants that are capable of finding optimal solution for a given problem instances. These ants at each step of the computation make probabilistic choices to include good solution component in partially 1 / 4

A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas Xiaoping Liu a , Xia Li a , Xun Shi b , Kangning Huang a & Yilun Liu a a School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, 510275, Guangdong, PR China b Department of Geography, Dartmouth College, Hanover, NH, 0375...

In this paper, an interactive version of the ParEGO algorithm is introduced for identifying most preferred solutions for computationally expensive multiobjective optimization problems. It enables a decision maker to guide the search with her preferences and change them in case new insight is gained about the feasibility of the preferences. At each interaction, the decision maker is shown a subs...

This paper introduces a deterministic method for capturing the Pareto front in multi-objective real parameter optimization problems. It is an effort to deal with multi-objective optimization problems that were until now confronted only by stochastic algorithms, specifically evolutionary algorithms. The method is based on a theoretical result that proves the equivalence of non-dominated points t...

Dynamic optimization using evolutionary algorithms is receiving increasing interests. However, typical test functions for comparing the performance of various dynamic optimization algorithms still lack. This paper suggests a method for constructing dynamic optimization test problems using multi-objective optimization (MOO) concepts. By aggregating different objectives of an MOO problem and chan...

In this article, a multi objective model is presented to select and allocate the order to suppliers in uncertainty condition and in a multi source, multi customer and multiproduct case in a multi period state at two levels of supply chain. Objective functions considered in this study as the measures to evaluate suppliers are cost including purchase, transportation and ordering costs, timely del...

In this study, the performance of the algorithms of whale, Differential evolutionary, crow search, and Gray Wolf optimization were evaluated to operate the Golestan Dam reservoir with the objective function of meeting downstream water needs. Also, after defining the objective function and its constraints, the convergence degree of the algorithms was compared with each other and with the absolut...