Eitaro Aiyoshi

Keio University, Edo, Tōkyō, Japan

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Publications (76)27.77 Total impact

  • Minoru Kanemasa, Eitaro Aiyoshi
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    ABSTRACT: In recent years, improvements in processing power have allowed the application of optimization methods to complicated large optimization problems. Among these methods, heuristic optimization techniques such as particle swarm optimization (PSO) have been a particular focus of attention because of their simplicity, performance, and easy software implementation. However, there is no solid theoretical foundation for analyzing the convergence of these algorithms, and in practice, their rate of convergence is often determined by the choice of parameters. For this reason, the algorithm's parameters must be tuned appropriately for each new optimization problem we want to solve, and in some cases the parameters must be varied as the algorithm is updated. In this paper, we combine a feedback element as an algorithm tuner with an original algorithm; the resulting algorithm is applied to the optimization problem in question, and we use genetic programming (GP) to generate tuning rules to automatically tune the algorithm, according to its current state, as the algorithm is updated. More specifically, we adopt PSO as a heuristic optimization method, and we augment PSO by using GP as a meta-algorithm to solve the learning problem of automatically generating tuning rules for the parameters in the PSO algorithm. This leads to the proposed method for generating parameter tuning rules to solve optimization problems more efficiently. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
    IEEJ Transactions on Electrical and Electronic Engineering 07/2014; 9(4). DOI:10.1002/tee.21986 · 0.34 Impact Factor
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    ABSTRACT: In this paper, we consider the use of a multi‐objective optimization method in order to obtain a preferred solution for the buffer material optimal design problem in the high‐level geological disposal of radioactive waste. The buffer material optimal design problem is formulated as a constrained multi‐objective optimization problem. Its Pareto optimal solutions are distributed evenly over the entire feasible region. Hence, we develop a search method to find a preferred solution easily for a decision maker from the Pareto optimal solutions, which are distributed evenly and broadly. In the preferred solution search method, a technique for visualization of a Pareto optimal solution set using a self‐organizing map is introduced into the satisficing trade‐off method, which is an interactive method for obtaining a Pareto optimal solution that satisfies a decision maker. We confirm the effectiveness of the preferred solution search method in the buffer material optimal design problem. © 2014 Wiley Periodicals, Inc. Electr Eng Jpn, 187(2): 17–32, 2014; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22634
    Electrical Engineering in Japan 04/2014; 187(2). DOI:10.1002/eej.22634 · 0.12 Impact Factor
  • Yuji Koguma, Eitaro Aiyoshi
    IEEJ Transactions on Electronics Information and Systems 01/2014; 134(9):1341-1347. DOI:10.1541/ieejeiss.134.1341
  • Takashi Okamoto, Eitaro Aiyoshi
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    ABSTRACT: In this study, we transform an optimization problem into a game problem by multiple decision makers. This transformation is called gamification of the optimization problem. Then, we discuss correspondence of the dynamics to search the rational solution of the game problem with the multi-point type optimization method to search the optimal solution of the original problem. Specifically, we propose a condition to compose objective functions of multiple decision makers who interfere with each other from the objective function of the original problem so that equivalence of the Nash equilibrium solution of the game problem to the optimal solution of the original problem is guaranteed. Then, correspondence of the gradient dynamics to solve the game problem with the multi-point type optimization method is discussed. We also show some numerical examples in order to explain the gamification of the optimization problem.
    Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics; 10/2013
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    ABSTRACT: In this paper, we consider a multi-objective optimization method in order to obtain a preferred solution for the buffer material optimal design problem in the high-level radioactive wastes geological disposal. The buffer material optimal design problem is formulated as a constrained multi-objective optimization problem. Its Pareto optimal solutions are distributed evenly on whole bounds of the feasible region. Hence, we develop a search method to find a preferred solution easily for a decision maker from the Pareto optimal solutions which are distributed evenly and vastly. In the preferred solution search method, the visualization technique of a Pareto optimal solution set using the self-organizing map is introduced into the satisficing trade-off method which is the interactive method to obtain a Pareto optimal solution that satisfies a decision maker. We confirm the effectiveness of the preferred solution search method in the buffer material optimal design problem.
    IEEJ Transactions on Electronics Information and Systems 01/2012; 132(7):1116-1127. DOI:10.1541/ieejeiss.132.1116
  • Kenichi Muranaka, Eitaro Aiyoshi
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    ABSTRACT: In this paper, we present a new type of hybrid methods for global optimization with Particle Swarm Optimization (PSO) and Differential Evolution (DE), which have attracted interests as heuristic and global optimization methods recently. Concretely, “p-best solutions” as the targets of PSO's particles are actuated by DE's evolutional mechanism in order to promote PSO's global searching ability. The presented hybrid method works effectively because PSO acts as a local optimizer and DE plays a role as a global optimizer. To evaluate performance of the hybridization, our method is applied to some benchmarks and is compared with the separated PSO and DE. Through computer simulations, it is certified that the proposed hybrid method performs fairy better than their separated algorithm.
    IEEJ Transactions on Electronics Information and Systems 01/2012; 132(7):1128-1135. DOI:10.1541/ieejeiss.132.1128
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    Eitaro Aiyoshi, Atsushi Maki, Takashi Okamoto
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    ABSTRACT: This paper proposes a Nash equilibrium model that applies continuous time replicator dynamics to the analysis of oligopoly markets. The robustness of the proposed simple Nash equilibrium model under the simultaneous constraints of allocation of product and market share using a simulation method to derive an optimal solution for production decisions by rival firms in oligopoly markets is tested by changing profit and cost function parameters, as well as the initial production values and market shares of the firms examined in this study. The effects of differences in conjectural variation and initial allocation of market share on the convergent values are considered, particularly in the case of corner solutions. This approach facilitates the understanding of the robustness of attaining equilibrium in an oligopoly market.
    Mathematics and Computers in Simulation 03/2011; 81:1518-1526. DOI:10.1016/j.matcom.2010.06.002 · 0.86 Impact Factor
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    ABSTRACT: In this paper, we propose a new global optimization model which is described as discrete time-varying inertial gradient dynamics. The proposed model has a autonomous damping term adjustment structure which takes the search history into consideration. In the proposed model, its dynamics destabilizes autonomously when its search point approaches to the best point in the search history. Thus, stagnation of search around the neighborhood of the best point is restrained, in consequence, global search is continued. Furthermore, in this paper, we analytically explain a characteristic of the search trajectory generated by the inertial gradient system. The characteristic is the implementation of intensive and diverse search in the attracting region for the inertial gradient system. Consequently, we propose a multi-point type search model in which search points are attracted to a promising region, where objective function value is small, by a coupling structure in order to make the attracting region the promising region. We confirm effectiveness of the proposed model through numerical experiments using several benchmark problems.
    01/2011; 46(11):642-650. DOI:10.9746/sicetr.46.642
  • Yoshinao Ishii, Takashi Okamoto, Eitaro Aiyoshi
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    ABSTRACT: Asynchronous Digenetic Particle Swarm Optimization for Global and Sustainable Search
    IEEJ Transactions on Electronics Information and Systems 01/2011; 131:626-634. DOI:10.1541/ieejeiss.131.626
  • Yuji Koguma, Atsuro Furusawa, Eitaro Aiyoshi
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    ABSTRACT: It is known that the evolutional computing methods hybridized with local search methods, which are called Memetic Algorithm, are efficient as global optimization methods. The memetic algorithms are divided into two classes of hybridization strategies, which are called Baldwinian and Lamarchian types. This paper is concerned with computational considerations for these two types of hybridization strategies in a case when Particle Swarm Optimization is used as evolutional algorithm from the standpoint of global optimization methods. Especially, applications to optimization problems with constraints described by disconnected plural sets, which are difficult to solve by usual methods, are considered.
    01/2011; 45(10):512-521. DOI:10.9746/sicetr.45.512
  • Kazuaki Masuda, Kenzo Kurihara, Eitaro Aiyoshi
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    ABSTRACT: In this paper, a method for solving min-max problems, especially for finding a solution which satisfies “min-max = max-min” condition, by using a modified particle swarm optimization (PSO) algorithm, is proposed. According to recent development in computer science, multi-point global search methods, most of which are classified into evolutionary computation and/or meta-heuristic methods, have been proposed and applied to various types of optimization problems. However, applications of them to min-max problems have been scarce despite their theoretical and practical importance. Since direct application of evolutionary computation methods to min-max problems wouldn't work effectively, a modified PSO algorithm for solving them is proposed. The proposed method is designed: (1) to approximate the minimized and maximized functions of min-max problems by using a finite number of search points; and, (2) to obtain one of “min-max = max-min” solutions by finding the minimum of the maximized function and the maximum of the minimized function. Numerical examples demonstrate the usefulness of the proposed method.
    Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Anchorage, Alaska, USA, October 9-12, 2011; 01/2011
  • Kazuaki Masuda, Eitaro Aiyoshi
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    ABSTRACT: Optimal Price Decision Problem for Simultaneous Multi-article Auction and Its Optimal Price Searching Method by Particle Swarm Optimization
    IEEJ Transactions on Electronics Information and Systems 01/2011; 131:461-467. DOI:10.1541/ieejeiss.131.461
  • Kazuaki Masuda, Eitaro Aiyoshi
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    ABSTRACT: An autoassociative memory which is modeled as the standard recurrent neural network (N.N.) is capable of storing multiple patterns and subsequently recalling one of them in response to an input signal. However, we found in our recent trials that it can't always recall correct patterns accurately. In this paper, we demonstrate such phenomena by numerical examples and identify the cause of memorization errors. We also propose an immediate solution to memorize correct patterns without fail by storing extra patterns at the same time.
    SICE Annual Conference (SICE), 2011 Proceedings of; 01/2011
  • Sodo Hiraoka, Takashi Okamoto, Eitaro Aiyoshi
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    ABSTRACT: Particle Swarm Optimization (PSO), which has attracted a great deal of attention as a global optimization method in recent years, has a drawback in that its continuous search based on its excellent dynamic characteristics can not be executed stably until the end of computation due to its much strong convergence trend. In this paper, we propose “Repetitive Search Guideline” which differs from a common guideline in the improved methods which have ever been proposed and by which the continuous search of PSO is achieved without lack of PSO's excellent dynamic characteristics due to the repetitive search in a promise area where objective function value is expected to be small. We consider four improved methods based on the proposed guideline, and then, their effectiveness are confirmed through applications to 100 variables multi-peaked benchmark problems.
    IEEJ Transactions on Electronics Information and Systems 11/2010; 128:1143-1153. DOI:10.1541/ieejeiss.128.1143
  • K. Masuda, K. Kurihara, E. Aiyoshi
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    ABSTRACT: This paper proposes a penalty method for solving nonlinear optimization problems with inequalities by the particle swarm optimization (PSO) algorithm. The proposed method is not only very simple but also useful. One should only search for the global solution of a series of unconstrained minimization problems simply by a standard PSO algorithm. It does not require to check the feasibility of search points during the search. Moreover, it is shown that the global best solution gets feasible as the penalty parameter is increased to a sufficiently but finitely large value. The proposed method is verified by numerical experiments to famous benchmark problems.
    Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on; 11/2010
  • Eitaro Aiyoshi, Kazuaki Masuda
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    ABSTRACT: On the basis of market fundamentalism, new types of social systems with the market mechanism such as electricity trading markets and carbon dioxide (CO2) emission trading markets have been developed. However, there are few textbooks in science and technology which present the explanation that Lagrange multipliers can be interpreted as market prices. This tutorial paper explains that (1) the steepest descent method for dual problems in optimization, and (2) Gauss-Seidel method for solving the stationary conditions of Lagrange problems with market principles, can formulate the mechanism of market pricing, which works even in the information-oriented modern society. The authors expect readers to acquire basic knowledge on optimization theory and algorithms related to economics and to utilize them for designing the mechanism of more complicated markets.
    IEEJ Transactions on Electronics Information and Systems 01/2010; 130(4):534-539. DOI:10.1541/ieejeiss.130.534
  • Kazuaki Masuda, Eitaro Aiyoshi
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    ABSTRACT: Associative memories are capable of memorizing particular patterns and recalling them from their partial information. Different from simple associative memory models based on Hopfield neural networks with sigmoid neurons, a particular model based on the chaotic neural network was also proposed for dynamic associative memory, which can generate various patterns from given information. However, the chaotic network model is so complicated that its behavior has not been analyzed well and can't be controlled easily. To the contrary, this paper shows that a discrete-time simple associative memory model with Euler's difference scheme has possibility to generate chaos. It follows that even such a simple model can be used for dynamic associative memory. Numerical examples also confirm the emergence of chaotic trajectories of the model and demonstrate their use for dynamic associative memory.
  • Yuji Koguma, Eitaro Aiyoshi
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    ABSTRACT: Particle Swarm Optimization (PSO), which has attracted special interest as a global optimization method recently, has a drawback in that its sustainable search can not be executed until the end of computation. In order to endow global searching abilities to PSO, repetition of unstable and stable states of the particles is necessary. In this paper, based on stability analysis of PSO's model, with considering its random numbers, we realize sustainable search by choosing system parameters on boundary region between unstable and stable states, and then introduce an optimization model with global searching abilities as a revision of the conventional PSO.
    IEEJ Transactions on Electronics Information and Systems 01/2010; 130(1):29-38. DOI:10.1541/ieejeiss.130.29
  • Source
    Eitaro Aiyoshi, Atsushi Maki
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    ABSTRACT: The present analysis applies continuous time replicator dynamics to the analysis of oligopoly markets. In the present paper, we discuss continuous game problems in which decision-making variables for each player are bounded on a simplex by equalities and non-negative constraints. Several types of problems are considered under conditions of normalized constraints and non-negative constraints. These problems can be classified into two types based on their constraints. For one type, the simplex constraint applies to the variables for each player independently, such as in a product allocation problem. For the other type, the simplex constraint applies to interference among all players, creating a market share problem. In the present paper, we consider a game problem under the constraints of allocation of product and market share simultaneously. We assume that a Nash equilibrium solution can be applied and derive the gradient system dynamics that attain the Nash equilibrium solution without violating the simplex constraints. Models assume that three or more firms exist in a market. Firms behave to maximize their profits, as defined by the difference between their sales and cost functions with conjectural variations. The effectiveness of the derived dynamics is demonstrated using simple data. The present approach facilitates understanding the process of attaining equilibrium in an oligopoly market.
    Mathematics and Computers in Simulation 05/2009; 79:2724-2732. DOI:10.1016/j.matcom.2008.10.009 · 0.86 Impact Factor
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    ABSTRACT: Among various control methods, model predictive control (MPC) becomes one of the major control strategies and has many successful applications. This paper presents an automatic tuning method of MPC using particle swarm optimization (PSO). One of the challenges in MPC is how the control parameters can be tuned for various target plants, and usage of PSO for automatic tuning is one of the solutions. The tuning problem of MPC is formulated as an optimization problem and PSO is applied as the optimization techniques. PSO is one of meta-heuristic methods which are known to search a global optimum at a relatively high ratio and with no use of a gradient. The numerical results for simple examples show the effectiveness of the proposed PSO-based automatic tuning method.
    SICE Annual Conference, 2008; 09/2008