This paper introduces a deterministic method for capturing the Pareto front in multi-objective real parameter optimization problems. It is an effort to deal with multi-objective optimization problems that were until now confronted only by stochastic algorithms, specifically evolutionary algorithms. The method is based on a theoretical result that proves the equivalence of non-dominated points t...

In the paper, a novel stochastic Multi-Objective Self-Organizing Migrating Algorithm (MOSOMA) is introduced. For the search of optima, MOSOMA employs a migration technique used in a single-objective Self Organizing Migrating Algorithm (SOMA). In order to obtain a uniform distribution of Pareto optimal solutions, a novel technique considering Euclidian distances among solutions is introduced. MO...

A memetic algorithm for tackling multiobjective optimization problems is presented. The algorithm employs the proven local search strategy used in the Pareto archived evolution strategy (PAES) and combines it with the use of a population and recombination. Verification of the new algorithm is carried out by testing it on a set of multiobjective 0/1 knapsack problems. On each problem instance, c...

In a previous paper a novel Generalized Multiobjective Multitree model (GMM-model) was proposed. This model considers for the first time multitree-multicast load balancing with splitting in a multiobjective context, whose mathematical solution is a whole Pareto optimal set that can include several results than it has been possible to find in the publications surveyed. To solve the GMM-model, in...

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distrib...

This paper presents an approach to adapt the suppression and scaling factor from a single input single output (SISO) dynamic matrix controller (DMC) thought a multiobjective optimization algorithm. To optimize, a nonlinear neural network (NN) process model is used, combined with a multiobjective evolutionary algorithm called SPEA II (Strength Pareto Evolutionary Algorithm) to find better contro...

It is a known fact that the performance of optimization algorithms for NP-Hard problems vary from instance to instance. We observed the same trend when we comprehensively studied multiobjective evolutionary algorithms (MOEAs) on a six benchmark instances of discrete time-cost trade-off problem (DTCTP) in a construction project. In this paper, instead of using a single algorithm to solve DTCTP, ...

A distribution generation (DG) multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper.The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. ...

A Strength Pareto Evolutionary Algorithm 2 (SPEA 2) approach for solving the multi-objective Environmental / Economic Power Dispatch (EEPD) problem is presented in this paper. In past fuel cost consumption minimization was aim (a single objective function) of economic power dispatch problem. Since clean air act amendments have been applied to reduce SO2 and NOX emissions from plants, utilities ...

Big data refers to the enormous heterogeneous being produced at a brisk pace by large number of diverse generating sources. Since traditional processing technologies are unable process big efficiently, is processed using newer distributed storage and frameworks. view materialization technique queries efficiently on these It generates valuable information, which can be used take timely decisions...