The high computational cost of population based optimization methods, such as multiobjective genetic algorithms (MOGAs), has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of simulation (objective/constraint functions) calls. We present a new multi-objective design optimizat...

* Department of Mechanical Engineering, Ubonratchathani University. ** Research and Development Center for Intelligent Systems, King Mongkut’s Institute of Technology North Bangkok. Abstract This paper presents the use of a multi-objective diversity control oriented genetic algorithm (MODCGA) for solving a closed-loop time-optimal path planning problem. The MODCGA is a result of the integration...

Target-like echoes from the use of active sonar, known as 'clutter', pose a problem to separate real from false contacts. A method for reducing the amount of clutter in tracking underwater targets is accomplished by using an iterative Multi-Objective Genetic Algorithm (MOGA). The optimization minimizes the position of the genetic population with the last given contact positions, as one objectiv...

In this paper, we propose a new genetic algorithm for multi-objective optimization problems. That is called “Neighborhood Cultivation Genetic Algorithm (NCGA)”. NCGA includes the mechanisms of other methods such as SPEA2 and NSGA-II. Moreover, NCGA has the mechanism of neighborhood crossover. Because of the neighborhood crossover, the effective search can be performed and good results can be de...

In this paper, Metallic Catalytic Converter (MCC) is installed in motorcycle exhausts to produce the minimum CO as well optimum engine power. The results from previous research were collected and then used predict best MCC design using Artificial Neural Network Multi-Objective Genetic Algorithm (ANN-MOGA). addition, ANN parameter tuning process was also carried out Taguchi method find initial w...

37 Abstract — Feature selection (FS) is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on ∩ fuzzy similarity measure...

11 A new scheme based on multi-objective hierarchical genetic algorithm (MOHGA) is proposed to extract interpretable rule-based knowledge from data. The approach is derived from the use of multiple objective genetic 13 algorithm (MOGA), where the genes of the chromosome are arranged into control genes and parameter genes. These genes are in a hierarchical form so that the control genes can mani...

Multi-objective genetic algorithms (MOGA) are used to optimize a low-thrust spacecraft control law for orbit transfers around a central body. A Lyapunov feedback control law called the Q-law is used to create a feasible orbit transfer. Then, the parameters in the Q-law are optimized with MOGAs. The optimization goal is to minimize both the flight time and the consumed propellant mass of the tra...

Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multiobjective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entr...

MULTI-OBJECTIVE OPTIMIZATION OF BLAST SIMULATION USING SURROGATE MODEL Toshihiro Tsuga, M.S. George Mason University, 2007 Thesis Director: Dr. Rainald Löhner A multi objective optimization approach using a Kriging model coupled with a Multi Objective Genetic Algorithm (MOGA) is applied to a blast damage maximization problem composed of two objectives, namely number of casualties and damage to ...