In the last two decades, multiobjective optimization has become mainstream because of its wide applicability in a variety of areas such engineering, management, the military and other fields. Multi-Objective Evolutionary Algorithms (MOEAs) play a dominant role in solving problems with multiple conflicting objective functions. They aim at finding a set of representative Pareto optimal solutions ...

Supervised alternative clusterings is the problem of finding a set of clusterings which are of high quality and different from a given negative clustering. The task is therefore a clear multi-objective optimization problem. Optimizing two conflicting objectives at the same time requires dealing with tradeoffs. Most approaches in the literature optimize these objectives sequentially (one objecti...

This paper presents a methodology for uncertainty quantification based multi-objective optimization of automotive body components under impact scenario. Conflicting design requirements arise as one tries, for example, to minimize structural mass while maximizing energy absorption of an automotive rail section under structural and occupant safety related performance measure constraints. Uncertai...

In this paper, a fuzzy multi-objective constrained optimization problem (FMCOP) is converted to a single-objective constrained optimization problem with equality and inequality constraints (SCOP) by using α−level set of the fuzzy vector and weighting approach. A trust-region algorithm for solving problem (SCOP) is introduced to obtain α−pareto optimal solutions of problem (FMCOP). An εα−stabili...

This paper proposes a method to solve multi-objective problems using improved Particle Swarm Optimization. We propose leader particles which guide other particles inside the problem domain. Two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. The first one is based on the mean of the m optimal particles and the second one is based on appoin...

This paper uses integrated Data Envelopment Analysis (DEA) models to rank all extreme and non-extreme efficient Decision Making Units (DMUs) and then applies integrated DEA ranking method as a criterion to modify Genetic Algorithm (GA) for finding Pareto optimal solutions of a Multi Objective Programming (MOP) problem. The researchers have used ranking method as a shortcut way to modify GA to d...

This paper presents a new strategy that can solve the Grid task scheduling problem with multiple objectives (NP-Hard) in polynomial time using evolutionary algorithms. The results obtained by our proposed algorithm were compared and evaluated against the -constraints classic Multi-Objective Optimization method, which uses the deterministic algorithm of Branch and Bound to find the real Pareto f...

In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade-offs between these objectives. A Pareto optimal solution set is achieved when a solution cannot be improved upon without degrading at least one of its objective criteria. This study investigated the application of multi-objective evolutionary algorithm (MOEA...

When we try to implement a multi-objective genetic algorithm (MOGA) with variable weights for finding a set of Pareto optimal solutions, one difficulty lies in determining appropriate search directions for genetic search. In our MOGA, a weight value for each objective in a scalar fitness function was randomly specified. Based on the fitness function with the randomly specified weight values, a ...

Abstract In this paper, multi-objective optimization (MOO) of Al2O3-water nanofluid flow in microchannel heat sinks (MCHS) with triangular ribs is performed using Computational Fluid Dynamics (CFD) techniques and Non-dominated Sorting Genetic Algorithms (NSGA II). At first, nanofluid flow is solved numerically in various MCHS with triangular ribs using CFD techniques. Finally, the CFD data will...