Abstract
Simulated Kalman filter (SKF) is an optimization algorithm which is inspired by Kalman filtering method. SKF was introduced as synchronous population-based algorithm. This work introduced a new variation of SKF which is SKF with asynchronous update mechanism, asynchronous-SKF (ASKF). In contrast to the synchronous implementation where the whole population go through each optimization step as a group, in ASKF an agent starts its optimization steps only after its preceding agent has completed all optimization steps. The performance of ASKF is compared against SKF using CEC2014 benchmark functions, where the ASKF is found to perform significantly better than the original SKF.
Highlights
A metaheuristic is an iterative generation process which guides a subordinate heuristic by combining intelligently different concepts for exploring and exploiting the search space so that a nearoptimal solution can be obtained [1]
Many works had been conducted on Simulated Kalman filter (SKF), where it had been modified for binary optimization problems [3] and combinatorial optimization problems [4,5,6,7]
The performance of asynchronous SKF (ASKF) is compared with the original SKF using CEC2014 benchmark function, where it is found that statistically ASKF is better than the original SKF
Summary
A metaheuristic is an iterative generation process which guides a subordinate heuristic by combining intelligently different concepts for exploring and exploiting the search space so that a nearoptimal solution can be obtained [1]. In 2015 a new metaheuristic algorithm, SKF, had been proposed for continuous unimodal optimization problems [2]. It was introduced as population-based metaheuristics, where the search for optimal solution is conducted by a group of agents. The agents of SKF work like Kalman filters, where they go through prediction, measurement, and estimation process in every iteration. As a population-based metaheuristic algorithm, the SKF’s agents conduct the search for optimal solution through information sharing. The evaluation of the candidate solutions found by SKF agents and the Kalman filter’s procedure of predict, measure and estimate are done iteratively. The performance of ASKF is compared with the original SKF using CEC2014 benchmark function, where it is found that statistically ASKF is better than the original SKF
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