A meta-heuristics algorithm for the global optimization of expensive, simulator evaluated, objective function
Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)
This paper presents the idea of a meta-heuristics algorithm called Evolutionary Controlled Clustering Algorithm (ECCA) designed for implementation in simulation optimization. The method focuses on localization of function optima neighborhoods. A Evolutionary Algorithm (EA) with soft selection and gene injection is used for finding basin of attraction. It operates over nodes of a grid created in a continuous parameter space. ECCA manipulates the grid density as well as the simulation accuracy. Clustered data is used for identification of the basin of attraction. Later, surrogate optimization is applied for local optima search. ECCA was optimized for operation in an uncertain and dynamically changing environment of simulation data. It was tested on the design of the shape of waveguide transition. The computer program can be executed concurrently on a multi-processor machine or on a grid of computers.
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