This paper presents a comprehensive comparison between the performance of state-of-the-art genetic algorithms NSGA-II, SPEA2 and IBEA and their differential evolution based variants DEMO, DEMO and DEMO. Experimental results on 16 numerical multiobjective test problems show that on the majority of problems, the algorithms based on differential evolution perform significantly better than the corr...

Selecting and prioritizing the most stable software requirements within a set of requirements and engaging them in releases that satisfy the most customers is a difficult task for the decision maker. Many methods have been employed to solve this type of problem. But we do not find many solutions that use Verbal Decision Analysis. Therefore, in this paper we aim to select and prioritize software...

In many real-world optimization problems the quality of solutions is determined with several fundamentally different objectives, such as, for example, cost, performance and profit. These objectives are often mutually conflicting thus yielding several optimal solutions, where each of them represents a different tradeoff between the objectives. Classicalmethods solve multiobjective optimization p...

Evolutionary Algorithms are recognized to be efficient to deal withMulti-objective Optimization Problems(MOPs) which are difficult to be solved with traditional methods. Here a newMulti-objective Optimization Evolutionary Algorithm named DGPS which is compound with Geometrical Pareto Selection Method (GPS), Weighted SumMethod (WSM) and Dynamical Evolutionary Algorithm (DEA) is proposed. Some fa...

We consider the problem of selecting investment components according to two partially opposed measures: the portfolio performance and its risk. We approach this within Markowitz’s model, considering the case of mutual funds market in Europe until July 2010. Comparisons were made on three multi-objective evolutionary algorithms, namely NSGAII, SPEA2 and IBEA. Two well-known performance measures ...

In this paper, we propose a calculation method of local dominance and enhance multiobjective evolutionary algorithms by performing a distributed search based on local dominance. We divide the population into several sub-populations by using declination angles of polar coordinate vectors in the objective space. We calculate local dominance for individuals belonging to each sub-population based o...

According to the traditional GA and EDA weakness, on the basis of MMEA, the orthogonal design initialization, convergence criterion and K-means clustering analysis method were introduced in this paper and it proposed a new model multi-objective evolutionary algorithm OMEA. The practice results showed that the OMEA had been greatly improved on both convergence and diversity of the solutions, rea...

This paper introduces an optimal active power filter design method to compensate simultaneously current harmonics and reactive power of a nonlinear load. The power filter consists of a passive RL low-pass filter placed in series with the load and a pure active filter which has RL elements connected in series with insulated gate bipolar transistors (IGBT) based voltage source converter. The filt...

Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve maximum power output minimum wind cost. This may be accomplished by applying single or multi-objective techniques. this paper, we apply objective hill-climbing algorithm (HCA) three evolutionary algorithms (NSGA-II, SPEA2 PESA-II) well-known bench...

In this paper, a many-objective optimization algorithm was developed using SPEA2 for system of four reservoirs in the Karun basin, including hydropower, municipal and industrial, agricultural, environmental objectives. For purpose, 53 years available data, hedging rules were two modes: with without applying fuzzy logic. used to optimize coefficients first 43 data last 10 test optimized rule cur...