In an emergency situation (e.g., tsunami, chemical spill, fire) it may be necessary to displace people to safer locations. Evacuation plans must be prepared so that these movements are properly organized. Based on the ACO (ant colony optimization) meta-heuristic we design a computational model to optimize a multi-objective path-finding associated to an evacuation planning problem. The results a...

Optimum controller placement in the presence of several conflicting objectives has received significant attention Software-Defined Wide Area Network (SD-WAN) deployment. Multi-objective evolutionary algorithms, like Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Particle Swamp Optimization (MOPSO), have proved helpful solving Controller Placement Problem (CPP) SD-WAN. However, these a...

Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) ( ) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and 3) the need for specifying a sharing parameter. In this paper, we suggest a nondominated sorting-based multiobjective EA (MOEA), called nondominate...

In this paper, a combinatorial reverse auction mechanism is proposed for selecting the most efficient service-providers for resolving sustained power interruptions in multiple regions of an electric power distribution company’s responsibility area. Through this mechanism, supplying the required service in each region is assigned to only one potential service-provider considering two criteria in...

in this paper, a novel mathematical model for a preemption multi-mode multi-objective resource-constrained project scheduling problem with distinct due dates and positive and negative cash flows is presented. although optimization of bi-objective problems with due dates is an essential feature of real projects, little effort has been made in studying the p-mmrcpsp while due dates are included i...

Planning and scheduling are as decision making processes which they have important roles in production systems and industries. According that, job shop scheduling is one of NPhard problems to solve multi-objective decision making approaches. So, the problem is known as uncertain with many variables in optimal solution view. Finding optimal solutions are essential task in scheduling of jobs betw...

In this paper we propose a novel approach for solving constrained multi-objective optimization problems using a steady state GA and reduced models. Our method called Objective Exchange Genetic Algorithm for Design optimization (OEGADO) is intended for solving real-world application problems that have many constraints and very small feasible regions. OEGADO runs several GAs concurrently with eac...

In this paper, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) based approach is presented for distribution system reconfiguration. In contrast to the conventional GA based methods, the proposed approach does not require weighting factors for conversion of multi-objective function into an equivalent single objective function. In order to illustrate the performance of the proposed method,...

The Protein Structure Prediction (PSP) problem is concerned about the prediction of the native tertiary structure of a protein in respect to its amino acids sequence. PSP is a challenging and computationally open problem. Therefore, several researches and methodologies have been developed for it. In this way, developers are working to integrate frameworks in order to improve their capabilities ...

Multi-objective evolutionary algorithms rely on the use of variation operators as their basic mechanism to carry out the evolutionary process. These operators are usually fixed and applied in the same way during algorithm execution, e.g., the mutation probability in genetic algorithms. This paper analyses whether a more dynamic approach combining different operators with variable application ra...