The most recent approaches of multi-objective optimization constitute application of meta-heuristic algorithms for which, parameter tuning is still a challenge. The present work hybridizes swarm intelligence with fuzzy operators to extend crisp values of the main control parameters into especial fuzzy sets that are constructed based on a number of prescribed facts. Such parameter-less particle ...

In this paper multi-objective genetic algorithms were employed for Pareto approach optimization of turboprop engines. The considered objective functions are used to maximize the specific thrust, propulsive efficiency, thermal efficiency, propeller efficiency and minimize the thrust specific fuel consumption. These objectives are usually conflicting with each other. The design variables consist ...

We introduce a multi-objective formulation of service-oriented testing, focusing on the balance between service price and reliability. We experimented with NSGA2 for this problem, investigating the effect on performance and quality of composition size, topology and the number of services discovered. For topologies small enough for exhaustive search we found that NSGA2 finds a pareto front very ...

Multiobjective 0-1 knapsack problem involving multiple knapsacks is a widely studied problem. In this paper, we consider a formulation of the biobjective 0-1 knapsack problem which involves a single knapsack; this formulation ismore realistic and hasmany industrial applications. Though it is formulated using simple linear functions, it is an NP-hard problem. We consider three different types of...

To simultaneously optimize multiple objective functions, several evolutionary multiobjective optimization (EMO) algorithms have been proposed. Nowadays, often set quality indicators are used when comparing the performance of those algorithms or when selecting “good” solutions during the algorithm run. Hence, characterizing the solution sets that maximize a certain indicator is crucial—complying...

This paper describes an exact ε-constraint method for bi-objective combinatorial optimization problems with integer objective values. This method tackles multi-objective optimization problems by solving a series of single objective subproblems, where all but one objective are transformed into constraints. We show in this paper that the Pareto front of bi-objective problems can be efficiently ge...

Recently, Evolutionary Trace Transform (ETT) has been developed to extract efficient features (called triple features) for invariant image identification using multi-objective evolutionary algorithms. This paper compares two methods of Evolutionary Trace Transform (method I and II) evolved through similar objectives by minimizing the withinclass variance (Sw) and maximizing the between-class va...

We investigate the trade-off between the robustness against random and targeted removal of nodes from a network. To this end we utilize the stochastic block model to study ensembles of infinitely large networks with arbitrary large-scale structures. We present results from numerical two-objective optimization simulations for networks with various fixed mean degree and number of blocks. The resu...

This paper visually demonstrates the effect of crossover operations on the performance of EMO algorithms through computational experiments on multi-objective 0/1 knapsack problems. In our computational experiments, we use the NSGA-II algorithm as a representative EMO algorithm. First we compare the performance of the NSGA-II algorithm between two cases: NSGA-II with/without crossover. Experimen...

In this paper, a novel clonal algorithm applied in multiobjecitve optimization (NCMO) is presented, which is designed from the improvement of search operators, i.e. dynamic mutation probability, dynamic simulated binary crossover (D-SBX) operator and hybrid mutation operator combining with Gaussian and polynomial mutations (GP-HM) operator. The main notion of these approaches is to perform more...