A combined compact genetic algorithm and local search method for optimizing the ARMA(1,1) model of a likelihood estimator

Abstract

In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function ln L(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Another comparison has been conducted to show that the I-CGA-SDA has fewer function evaluations, minimum search space percentage, faster convergence speed and has a higher optimal precision than that of the HGA.

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

“…Tis can be seen as an enhanced exploration phase, where the cMeta is used to refne the new initial point. Tis can also be done with an opposite approach where the cMeta provides solutions that are refned with a local searcher as, e.g., steepest descent [223], or problem-specifc local search operators [222,224,225].…”

“…Te most interesting areas of application of compact optimisation in the discrete domain include the Travelling Salesman Problem (TSP) [224,261]; determining minimum set primers in Polymerase Chain Reaction (PCR) [262]; task scheduling in grid computing environments [263]; protein folding [264]; object recognition [265,266]; soft decision decoding [267,268]; minimising the number of coding operations required in multicast based on network coding [222]; estimating the parameters of the maximum log-likelihood function of a frst-order moving average model MA [269] and a mixed model ARMA (1, 1) [223]; optimising the aggregation of multiple similarity measures to obtain a single similarity metric for ontology matching [270]; optimising ontology alignment [271]; designing multiple input multiple output wireless communication systems [272].…”

Multiobjective Optimization Based Vessel Collision Avoidance Strategy Optimization

“…With the appearance and development of new navigation equipment like AIS [5,6], advanced computer technology, and so forth, the application of intelligent optimization algorithm has been used for collision avoidance strategy searching. Genetic algorithm (GA) is a popular heuristic algorithm, which has been used for many subjects, such as system identification [7,8], supply chain [9], and scheduling problem [10].Ś mierzchalski and Michalewicz [11], Szlapczynski [12], and Szlapczynski and Szlapczynska [13] first made use of genetic algorithm to plan the route of vessel in static or dynamic environment in order to avoid obstacles. Similar heuristic optimization algorithms have been used by other 2 Mathematical Problems in Engineering researchers: GA is used to find the optimal path and manoeuvres in collision avoidance [14][15][16].…”