This paper studies charging scheduling problem of electric vehicles (EVs) in the scale of a microgrid (e.g., a university or town) where a set of charging stations are controlled by a central aggregator. A bi-objective optimization problem is formulated to jointly optimize total charging cost and user convenience. Then, a close-to-optimal online scheduling algorithm is proposed as solution. The...

A self-adaptive Pareto Evolutionary Multi-objective Optimization (EMO) algorithm is proposed for evolving controllers for a virtually embodied robot. The main contribution of the self-adaptive Pareto approach is its ability to produce controllers with different locomotion capabilities in a single run, therefore reducing the evolutionary computational cost significantly. The aim of this paper is...

In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two approaches: (1) moment bounding approach and (2) likelihood-based approach. This paper first proposes a ...

In multiobjective optimization methods, the multiple conflicting objectives are typically converted into a single objective optimization problem with the help of scalarizing functions and such functions may be constructed in many ways. We compare both theoretically and numerically the performance of three classification-based scalarizing functions and pay attention to how well they obey the cla...

In this paper, we propose a new constraint-handling technique for evolutionary algorithms which is based on multiobjective optimization concepts. The approach uses Pareto dominance as its selection criterion, and it incorporates a secondary population. The new technique is compared with respect to an approach representative of the state-of-the-art in the area using a well-known benchmark for ev...

Over the past few years, swarm intelligence based optimization techniques such as ant colony optimization and particle swarm optimization have received considerable attention from engineering researchers. These algorithms have been used in the solution of various structural optimization problems where the main goal is to minimize the weight of structures while satisfying all design requirements...

A lot of methods for analog filter group delay optimization have been developed. In the article an original approach to the evaluation of the optimization methods is presented. Our approach is based on the original representation of the optimization results in a polar space. In that space there are very interesting spirals indicating the optimization efficiency directly and clearly. This approa...

In this paper particle swarm optimization (PSO) is used for a design optimization of a linear permanent magnet synchronous motor (LPMSM) considering ultra low thrust force ripples, low magnet consumption, improved efficiency and thrust. The influence of PM material is discussed, too and the modular poles are proposed to achieve the best characteristic. PM dimensions and material, air gap and mo...

in this paper, the problem of layout optimization for x-bracing of steel frames is studied using the ant system (as). a new design method is employed to share the gravity and the lateral loads between the main frame and the bracings according to the requirements of the ibc2006 code. an algorithm is developed which is called optimum steel designer (osd). an optimization method based on an approx...

ant colony optimisation (aco) algorithm and adaptive refinement mechanism are used in this paper for solution of optimization problems. many of the real engineering problems are، however، of continuous nature and finding their solution by discrete ant based algorithms requires discretisation of the decision variables in which affected the convergence and performance of the algorithm. in this pa...