Abstract
One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper. The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm's exploration and exploitation capability. A comprehensive test suite of standard benchmark problems with different natures, i.e., unimodal/multimodal and separable/non-separable, was used to test the convergence power of the proposed CTbADE algorithm. Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness, solution quality, and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms, such as jDE, CoDE, and EPSDE algorithms. This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computational models with a high number of parameters to adjust during the problem-solving process.
Highlights
Differential evolution (DE) is a stochastic algorithm introduced by Storn and Price [1]
This paper presents a convergence tracking over time based parametric adaptive version of differential evolution algorithms
The concept of a small memory based on a user defined learning period is used in the sequence of control parameters of the algorithm to improve the convergence behavior of DE algorithms and escaping from the stagnation problem
Summary
Differential evolution (DE) is a stochastic algorithm introduced by Storn and Price [1]. Differential evolution is a very powerful heuristic global search evolutionary algorithm for the solution of real parameter optimization. To evolve a current population, a DE algorithm uses crossover, selection, and mutation operators. DE algorithms have crossover, mutation, and selection operators in order to evolve the current population to locate optima within the given search space. The CR control parameter contributes to population diversity; greater diversity increases the algorithm’s exploration power and a smaller value reduces population diversity and focuses on the exploitation capability [18,19]. The issue with a CNN is the slow convergence speed to train and test the image data in order to detect Covid-19 infection [25]. The main contribution of this research work is to present a novel convergence track based adaptive differential evolution (CTbADE) algorithm. 2 Related Work This sections contains details of the original DE algorithm and a literature review
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