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Dynamic multi-objective evolutionary algorithms (Dynamic MOEAs) use the evolutionary algorithms to solve the dynamic multi-objective optimization problems (DMOPs). It has become one of the hot areas of research. The challenge of DMOPs is that the objective functions, the constraints or the parameters may change over time. This paper tries to provide a comprehensive overview of the related work, which...
With the development of national economy and the improving people's living standards, the supply capacity of distribution network, power quality and reliability requirements are increasing, the power company is facing many new challenges, one important issue is how to improve the quality and reliability of power supply at the same time, as much as possible to reduce operating, maintenance and construction...
This paper proposes a Multi-Objective Optimization (EMOO) algorithm is used to tune the Proportional Integral (PI) speed regulator in the Permanent Magnet DC (PMDC) motor drive system It aims at achieving good robustness and struck at global optima. Calculus based methods including gradient approaches mainly search for local optimum solutions, rather than global optimum. Due to the drawbacks that...
A design procedure for synthesizing wideband conformal antenna arrays based on a multi-objective evolutionary algorithm is presented. In this paper, raised power series (RPS) are employed as a simple yet effective way to introduce aperiodicity into a conformal semi-circular phased antenna array for achieving wideband performance. Unlike conventional linear array synthesis methods where, for example,...
Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increases the provider's profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop...
The memory BIST insertion involves the simultaneous optimization of several conflicting and competing objectives such as test time and power consumption during the test execution procedure. In this paper, a new memory BIST methodology is proposed which optimizes area overhead, test power and test time. It exploits Genetic Algorithms to find a set of Pareto optimal solutions. Since the designer is...
In response to the ongoing discussion on how electric power distribution systems should evolve under the Smart Grid Initiative, an optimization problem is defined to simultaneously determine optimal locations for Distributed Generation (DG) and feeder interties in a legacy radial distribution system to improve reliability in the islanded mode of operation. For that purpose, an evolutionary approach...
This paper proposes an integrated method based on multi-objective optimization (NSGA II) and multi-attribute decision making (TOPSIS) to analyze the optimal flow distribution of e-waste reverse logistics network. Within the established multi-objective model, objectives concerning economic (total profit), ecological (accumulated energy consumption) performances and loss are considered. For the multi-objective...
In order to solve the problem that the parameters of cloud model are difficult to get and the weight of performance index is difficult to obtain when adopt single objective optimization algorithm. A new design of cloud model controller based on multi-objective optimization is proposed. In the first stage, set the system overshoot, settling time, and ITAE index as the optimization objectives, then...
As metal products, bearings are prone to rust provided in storage for a long time. To improve products quality on bearing rusting during storage, this paper proposes a concept of satisfactory level on bearing rusting and designs the expressing function. With regard to the stock-in operations, a multi-objective optimization model is proposed for allocating storage at the time of stock-in, so as to...
Vector Evaluated Particle Swarm Optimization (VEPSO) has been successfully applied to various applications. However, the VEPSO actual performance is still uncertain. Hence, this paper will evaluate the VEPSO performance in term of convergence and diversity ability using Generalized Distance and Spread measurement respectively. Simulation with ZDT benchmark test problems show VEPSO is weak in solving...
We consider a berth allocation and quay crane assignment problem (BACAP) to concurrently optimize the two objectives: operation cost and customer satisfaction. We formulate the multi-objective BACAP as a mixed-integer nonlinear programming model. The latter objective is based on vessel departure tardiness which is measured from the perspective of behavioral operations research. A multi-objective genetic...
In this paper, system identification of the non-linear dynamic system based on optimized Volterra model structure is considered. Model structure selection is an important step in system identification, which involves the selection of variables and terms of a model. The important issue is choosing a compact model representation where only significant terms are selected among all the possible ones beside...
Spectroscopic ellipsometry is applied to the characterization of UV patterned channel waveguides to obtain refractive index contrast and surface contraction. The waveguides were prepared with organic-inorganic di-ureasils hybrids modified with zirconium tetra-propoxide, processed as thin films in silica on silicon substrates. The channel waveguides were produced by direct writing using UV laser radiation...
In view of the coal plow of the load and energy consumption question, optimized the cutting and parameter movement for plow body of on improved genetic algorithm multi-objective optimization strategy. The optimized results indicate that the cutting consumption reduces 16.96%, productivity capability rises 23.38%, loading area uniformity rises 16.62%, productivity capability of coal plow obtains obviously...
Although feature selection has been proven to be very effective in machine learning and pattern classification applications, it has not been widely practiced in the area of image annotation and retrieval. This paper presents a method of selecting a near optimal to optimal subset of statistical texture descriptors in efficient representation and retrieval of ultrasound medical images. An objective...
A hybrid genetic algorithm is proposed for multi-objective flexible job-shop scheduling problem, where the time, cost and equipment utilization rate are used as objective functions. First, the scheduling model for this problem is set up. Second, the matrix chromosome based on job-scheduling encode is adopted and it makes the decode and the use of belief operator much easier. Third, The objective functions...
In this paper, we presented a multi-objective optimization model of fixed-time signal control of unsaturated intersections. The performance indices taken into consideration include total volume, average delay, average stop frequency, average queue length of vehicular traffic at signalized isolated intersections. A multi-objective genetic algorithm was given to solve the model based on Non-dominated...
The crossover operator plays an important role in a genetic algorithm, which produces two or more offspring for each pair of parents. With the help of the crossover operator, the genetic algorithm can explore the search space effectively. In this paper, we propose a new crossover operator called elliptical crossover operator, which can explore the search domain effectively. A local search scheme is...
Protein structure prediction is one of the most important problems in bioinformatics and structural biology. This work proposes a novel and suitable methodology to model protein structure prediction with atomic-level detail by using a parallel multi-objective ab initio approach. In the proposed model, i) A trigonometric representation is used to compute backbone and side-chain torsion angles of protein...
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