Tomoyuki Hiroyasu

SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2

Multi-objective optimization methods are essential to resolve real-world problems as most involve several types of objects. Several multi-objective genetic algorithms have been proposed. Among them, SPEA2 and NSGA-II are the most successful. In the present study, two new mechanisms were added to SPEA2 to improve its searching ability a more effective crossover mechanism and an archive mechanism to maintain diversity of the solutions in the objective and variable spaces. The new SPEA2 with these two mechanisms was named SPEA2+. To clarify the characteristics and effectiveness of the proposed method, SPEA2+ was applied to several test functions. In the comparison of SPEA2+ with SPEA2 and NSGA-II, SPEA2+ showed good results and the effects of the new mechanism were clarified. From these results, it was concluded that SPEA2+ is a good algorithm for multi-objective optimization problems.

The new model of parallel genetic algorithm in multi-objective optimization problems - divided range multi-objective genetic algorithm

Proposes a divided-range multi-objective genetic algorithm (DRMOGA), which is a model for the parallel processing of genetic algorithms (GAs) for multi-objective problems. In the DRMOGA, the population of GAs is sorted with respect to the values of the objective function and divided into sub-populations. In each sub-population, a simple GA for multi-objective problems is performed. After some generations, all the individuals are gathered and they are sorted again. In this model, the Pareto-optimal solutions which are close to each other are collected into one sub-population. Therefore, this algorithm increases the calculation efficiency and a neighborhood search can be performed. Through numerical examples, the following facts become clear: (i) the DRMOGA is a very suitable GA model for parallel processing, and (ii) in some cases it can derive better solutions compared to both the single-population model and the distributed model.

Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological Model

In this paper, the simulation of the multi-objective optimization problem of a diesel engine is performed using the phenomenological model of a diesel engine and the genetic algorithm. The target purpose functions are Specific fuel consumption, NOx, and Soot. The design variable is a shape of injection rate. In this research, we emphasize the following three topics by applying the optimization techniques to an emission problem of a diesel engine. Firstly, the multiple injections control the objectives. Secondly, the multi-objective optimization is very useful in an emission problem. Finally, the phenomenological model has a great advantage for optimization. The developed system is illustrated with the simulation examples.

Reduction of Heavy Duty Diesel Engine Emission and Fuel Economy with Multi-Objective Genetic Algorithm and Phenomenological Model

In this study, a system to perform a parameter search of heavy-duty diesel engines is proposed. Recently, it has become essential to use design methodologies including computer simulations for diesel engines that have small amounts of NOx and SOOT while maintaining reasonable fuel economy. For this purpose, multi-objective optimization techniques should be used. Multi-objective optimization problems have several types of objectives and they should be minimized or maximized at the same time. There is often a trade-off relationship between objects and derivation of the Pareto optimum solutions that express the relationship between the objects is one of the goals in this case. The proposed system consists of a multiobjective genetic algorithm (MOGA) and phenomenological model. MOGA has strong search capability for Pareto optimum solutions. However, MOGA requires a large number of iterations. Therefore, for MOGA, a diesel combustion simulator that can express combustion precisely with small calculation cost is essential. Phenomenological models can simulate diesel engine combustions precisely with small calculation cost. Therefore, phenomenological models are suitable for MOGA. In the optimization simulations, fuel injection shape, boost pressure, EGR rate, start angle of injection, duration angle of injection, and swirl ration were chosen as design variables. The values of these design variables were optimized to reduce SFC, NOx, and SOOT. Through the optimization simulations, the following five points were made clarified. First, the proposed system can find the Pareto optimum solutions successfully. Second, MOGAs are very effective to derive the solutions. Third, phenomenological models are very suitable for MOGAs, as they can perform precise simulations with small calculation cost. Fourth, multi-pulse injection shape can affect the amounts of SFC, NOx, and SOOT. Finally, parameter optimization is essential for in diesel engine design.

Multiobjective clustering with automatic k-determination for large-scale data

Web mining - data mining for web data - is a key factor of web technologies. Especially, web behavior mining has attracted a great deal of attention recently. Behavior mining involves analyzing the behavior of users, finding patterns of user behavior, and predicting their subsequent behaviors or interests. Web behavior mining is used in web advertising systems or content recommendation systems. To analyze huge amounts of data, such as web data, data-clustering techniques are usually used. Data clustering is a technique involving the separation of data into groups according to similarity, and is usually used in the first step of data mining. In the present study, we developed a scalable data-clustering algorithm for web mining based on existent evolutionary multiobjective clustering algorithm. To derive clusters, we applied multiobjective clustering with automatic k-determination (MOCK). It has been reported that MOCK shows better performance than k-means, agglutination methods, and other evolutionary clustering algorithms. MOCK can also find the appropriate number of clusters using the information of the trade-off curve. The k-determination scheme of MOCK is powerful and strict. However the computational costs are too high when applied to clustering huge data. In this paper, we propose a scalable automatic k-determination scheme. The proposed scheme reduces Pareto-size and the appropriate number of clusters can usually be determined.

A parallel genetic algorithm with distributed environment scheme

Introduces an alternative approach to relieving the task of choosing optimal mutation and crossover rates by using a parallel and distributed GA with distributed environments. It is shown that the best mutation and crossover rates depend on the population sizes and the problems, and those are different between a single and multiple populations. The proposed distributed environment GA uses various combination of the parameters as the fixed values in the subpopulations. The excellent performance of the new scheme is experimentally recognized for a standard test function. It is concluded that the distributed environment GA is the fastest way to gain a good solution under the given population size and uncertainty of the appropriate crossover and mutation rates.

Distributed optimal control of lighting based on stochastic hill climbing method with variable neighborhood

In this research, a smart lighting system based on a new autonomous distributed control method was developed to control lighting using illuminance sensors. By using infrared ray communication, one illuminance sensor sends a luminance control directive to several lighting fixtures located nearby. The luminance is made to change randomly within a fixed range to optimize the illuminance using the stochastic hill climbing method. The result of operational experiments using illuminance sensors that were preset to the target illuminance showed that the brightness at specified locations approached the target illuminance that the illuminance sensors were set to. The electrical power consumed by the lighting system was also minimized. Moreover, in comparison with lighting driven autonomous distributed controls of smart lighting systems, better results were obtained, indicating that the method is an effective new distributed control method.

Comparison study of SPEA2+, SPEA2, and NSGA-II in diesel engine emissions and fuel economy problem

Recently, the technology that can control NOx and Soot values of diesel engines by changing the electronically controllable parameters has been developed. However, there is a trade-off relationship between fuel economy and NOx values. Therefore, the diesel engines that can change their characteristics with along to the driving environment should be emerged in the future. For designing these kinds of engines, the Pareto solutions that can express the trade-off between fuel economy and NOx values are needed. In that case, the derived non dominated solutions should have the diversity not only in the objective space but also in the design variable space. SPEA2+ is one of multi objective genetic algorithms and is developed based on SPEA2. The derived non dominated solutions by SPEA2+ have the diversity in both objective space and design variable space. In this study, the diesel engines that have high fuel economy and small amounts of NOx and Soot are designed by SEPA2+. The results are compared with those of SPEA2 and NSGA-II. From the discussions, it is found that the solutions of SPEA2+ have the diversity not only in the objective space but also in the design variable space. These characteristics are very suitable for designing diesel engines whose parameters are changing against the driving environment

Distributed genetic algorithms with a new sharing approach in multiobjective optimization problems

In this paper, a new distributed genetic algorithm for multiobjective optimization problems is proposed. In this approach, the island model is used with a distributed genetic algorithm and an operation of sharing for Pareto-optimum solutions is performed with the total population. In multiobjective optimization problems, the Pareto-optimum solutions should be derived for designers. Because the Pareto-optimum solutions are the set of optimum solutions that are in the relationship of trade-off, not only the accuracy but also the diversity of the solutions should be high. The effect of the distributed populations leads to the high accuracy and the sharing effect leads to the high diversity of solutions. These effects are examined and discussed through some numerical examples that have more than three objective functions.

Distributed probabilistic model-building genetic algorithm

In this paper, a new model of Probabilistic Model-Building Genetic Algorithms (PMBGAs), Distributed PMBGA (DPMBGA), is proposed. In the DPMBGA, the correlation among the design variables is considered by Principal Component Analysis (PCA) when the offsprings are generated. The island model is also applied in the DPMBGA for maintaining the population diversity. Through the standard test functions, some models of DPMBGA are examined. The DPMBGA where PCA is executed in the half of the islands can find the good solutions in the problems whether or not the problems have the correlation among the design variables. At the same time, the search capability and some characteristics of the DPMBGA are also discussed.