Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(mN3) computational complexity (where m is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algo...

Several problems in the area of financial optimization can be naturally dealt with optimization techniques under multiobjective approaches, followed by a decision-making procedure on the resulting efficient solutions. The problem of portfolio optimization is one of them. This chapter studies the use of evolutionary multiobjective techniques to solve such problems, focusing on Venezuelan market ...

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload ...

Determining the contribution of an agent to a system-level objective function (credit assignment) is a key area of research in cooperative multiagent systems. Multi-objective optimization is a growing area of research, though mostly focused on single agent settings. Many real-world problems are multiagent and multi-objective, (e.g., air traffic management, scheduling observations across multipl...

NSGA ( [5]) is a popular non-domination based genetic algorithm for multiobjective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGAII ( [3]) was developed, which has a better sorting algorithm , incorporates elitism...

This work proposes Adaptive e-Ranking to enhance Pareto based selection, aiming to develop effective many-objective evolutionary optimization algorithms. eRanking fine grains ranking of solutions after they have been ranked by Pareto dominance, using a randomized sampling procedure combined with e-dominance to favor a good distribution of the samples. In the proposed method, sampled solutions k...

In dynamic service-oriented environment, service monitoring could provide reliability improvement to service composition as well as cost increase. To reduce the overall cost brought by monitoring, existing literatures proposed to decrease the number of monitors through monitoring the most reliability-sensitive services. However, the optimal monitoring rate for those monitors was not taken into ...

Feedback controls are usually designed to achieve multiple and often conflicting performance goals. These incommensurable objectives can be found in both time and frequency domains. For instance, one may want to design a control system such that the closed-loop system response to a step input has a minimum percentage overshoot (Mp), peak time (tp), rise time (tr), settling time (ts), tracking e...

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...

Nowadays, the subject of vision metrology network design is local enhancement of the existing network. In the other words, it has changed from first to third order design concept. To improve the network, locally, some new camera stations should be added to the network in drawback areas. The accuracy of weak points is enhanced by the new images, if the related vision constraints are satisfied si...