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This paper points out that meta-heuristics should have not only robustness and adaptability to problems with different structure but also adjustability of parameters included in their algorithms. Particle swarm optimization (PSO), whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, a new parameter is introduced into PSO on the basis of the proximate optimality principle (POP). In this paper, we propose adaptive PSO and the effectiveness and the feasibility of the proposed approach are demonstrated on simulations using some typical nonconvex optimization problems. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 151(3): 41–49, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20077 [ABSTRACT FROM AUTHOR]

The paper presents an analytical description of a state-dependent assignment policy, where different assignments of attributes to objects are employed under different states. Various considerations, other than optimizing an objective function, present themselves for such cases—for example, the existence of groups of assignees with certain levels of intragroup assignment uniformity, the stability of the policy over states, the presence of a revealed preference ordering of the assignments, and others. An approach towards a quantitative treatment of such features of a state-dependent assignment policy is discussed, and in particular a uniformity measure is defined and its properties are investigated. Potential uses of the model are suggested for urban emergency services, questionnaire analysis, task allocation, medical diagnosis, and dynamic computer program behavior. Three implementations for questionnaire analysis are reported. [ABSTRACT FROM AUTHOR]