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An adaptive differential evolution algorithm witha bound adjustment strategy for solving nonlinear parameter identification problems

Wongsa Watchara, Puphasuk Pikul, Wetweerapong Jeerayut

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2024

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T. 14, nr 2

119--126

EN

Real-world parameter identification problems require determining the bounds that cover the unknown solutions. This paper presents an adaptive differential evolution algorithm with a bound adjustment strategy (ADEBAS) for solving nonlinear parameter identification problems. The adjustment strategy detects the parameter-bound violations of mutant vectors during the evolution process and gradually extends the bounds. The algorithm adaptively uses two mutation strategies and two ranges of crossover rate to balance the population diversity and convergence speed. Experimental results show that ADEBAS can solve 24 nonlinear regression tasks from the National Institute of Standards and Technology benchmark with accurate estimation and reliability. It also outperforms the compared methods on real-world parameter identification problems.

PL

Problemy identyfikacji parametrów w świecie rzeczywistym wymagają określenia granic, które pokrywają nieznane rozwiązania. W artykule przedstawiono adaptacyjny różniczkowy algorytm ewolucyjny ze strategią dostosowywania granic (ADEBAS) do rozwiązywania nieliniowych problemów identyfikacji parametrów. Strategia dostosowywania wykrywa naruszenia granic parametrów zmutowanych wektorów podczas procesu ewolucji i stopniowo rozszerza granice. Algorytm adaptacyjnie wykorzystuje dwie strategie mutacji i dwa zakresy szybkości krzyżowania, aby zrównoważyć różnorodność populacji i szybkość zbieżności. Wyniki eksperymentów pokazują, że ADEBAS może rozwiązać 24 zadania regresji nieliniowej z benchmarku National Institute of Standards and Technology z dokładnym oszacowaniem i niezawodnością. Przewyższa również porównywane metody w rzeczywistych problemach identyfikacji parametrów.

2

Adaptive differential evolution algorithm with a pheromone-based learning strategy for global continuous optimization

Singsathid Pirapong, Puphasuk Pikul, Wetweerapong Jeerayut

Foundations of Computing and Decision Sciences

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2023

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Vol. 48, No. 2

243--266

EN

Differential evolution algorithm (DE) is a well-known population-based method for solving continuous optimization problems. It has a simple structure and is easy to adapt to a wide range of applications. However, with suitable population sizes, its performance depends on the two main control parameters: scaling factor (F) and crossover rate (CR). The classical DE method can achieve high performance by a time-consuming tunning process or a sophisticated adaptive control implementation. We propose in this paper an adaptive differential evolution algorithm with a pheromone-based learning strategy (ADE-PS) inspired by ant colony optimization (ACO). The ADE-PS embeds a pheromone-based mechanism that manages the prob- abilities associated with the partition values of F and CR. It also introduces a resetting strategy to reset the pheromone at a specific time to unlearn and relearn the progressing search. The preliminary experiments find a suitable number of subintervals (ns) for partitioning the control parameter ranges and the reset period (rs) for resetting the pheromone. Then the comparison experiments evaluate ADE-PS using the suitable ns and rs against some adaptive DE methods in the literature. The results show that ADE-PS is more reliable and outperforms several well-known methods in the literature.

3

"An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization"

Puphasuk Pikul, Wetweerapong Jeerayut

Foundations of Computing and Decision Sciences

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2020

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Vol. 45, No. 2

97--124

EN

Designing an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at efficiency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and difficulties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.