Evolutionary algorithms have been recognized to be suitable for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier‎. ‎This paper applies an evolutionary optimization scheme‎, ‎inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategi...

Permanent magnet synchronous generators (PMSG) have a huge potential for direct-drive wind power applications. Therefore, optimal design of these generators is necessary to maximize their efficiency and to reduce their manufacturing cost and total volume. In this paper, an optimal design of a six-phase 3.5 KW direct-drive PMSG to generate electricity for domestic needs is performed. The aim of ...

This paper presents an improved multi-objective evolutionary algorithm (IMOEA) for the design of planar steel frames. By considering constraints as a new objective function, single objective optimization problems turned to multi objective optimization problems. To increase efficiency of IMOEA different Crossover and Mutation are employed. Also to avoid local optima dynamic interference of mutat...

Dynamic Multi-Objective Optimization Problems (DMOPs) and Many-Objective (MaOPs) are two classes of the optimization field that have potential applications in engineering. Modified Evolutionary Algorithms hybrid approaches seem to be suitable effectively deal with such problems. However, standard Crow Search Algorithm has not been considered for either DMOPs or MaOPs date. This paper proposes a...

A mobile ad-hoc network (MANET), a set of wirelessly connected sensor nodes, is a dynamic system that executes hop-by-hop routing independently with no external help of any infrastructure. Proper selection of cluster heads can increase the life time of the Ad-hoc network by decreasing the energy consumption. Although different methods have been successfully proposed by researchers to tackle...

Heuristic optimization provides a robust and efficient approach for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. The convergence rate and suitable diversity of solutions are of great importance for multi-objective evolutionary algorithms. The focu...