The majority of real-world problems encountered by engineers involve simultaneous optimization of competing objectives. In this case instead of single optima, there is a set of alternative trade-offs, generally known as Pareto-optimal solutions. The use of evolutionary algorithms Pareto GA, which was first introduced by Goldberg in 1989, has now become a sort of standard in solving Multiobjecti...

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...

mance of multiple objective evolutionary algorithms on distribution system desi gn problem – computational experiment, in: Abstract. The paper presents a comparative experiment with fo ur multiple objective evolutionary algorithms on a real life co mbinatorial optimization problem. The test problem corresponds to the design of a distribution system. The experiment compares performance of a Pare...

Many evolutionary algorithms have been lately developed for solving multiobjective problems, appealing or not to the Pareto optimality concept. Although, the evolutionary techniques for multiobjective optimization confront with several issues as: elitism, diversity of the population, or efficient settings for the specific parameters of the algorithm. In this paper, we propose a new evolutionary...

The transit network design problem (TNDP) aims to find a set of routes and corresponding frequencies for an urban public transportation system. We model the TNDP as a multi-objective combinatorial optimization problem whose resolution involves finding a Pareto front that represents different trade-off levels between opposite objectives, the travel and waiting times and the required fleet of bus...

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...

Evolutionary optimization algorithms have been used to solve multiple objective problems. However, most of these methods have focused on search a sufficient Pareto front, and no efforts are made to explore the diverse Pareto optimal solutions corresponding to a Pareto front. Note that in semi-obnoxious facility location problems, diversifying Pareto optimal solutions is important. The paper the...

This paper reports an evolutionary approach called Scatter Search, that uses the concept of Pareto optimality to obtain a good approximate Pareto frontier. In order to designate a subset of strategies to be a reference solutions a choice function called Kramer Selection is used. A variant of measure of Kemen-Snell may be used, in our case study, in order to find a diverse set to complement the ...

distribution centers (dcs) play important role in maintaining the uninterrupted flow of goods and materials between the manufacturers and their customers.this paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. an evolutionary algorithm named non-dominated sorting ant colony optimization (nsaco) is used as the optimi...

This paper addresses an unrelated multi-machine scheduling problem with sequence-dependent setup time, release date and processing set restriction to minimize the sum of weighted earliness/tardiness penalties and the sum of completion times, which is known to be NP-hard. A Mixed Integer Programming (MIP) model is proposed to formulate the considered multi-criteria problem. Also, to solve the mo...