Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design

Abstract

In this paper, a multi-strategy fusion enhanced Honey Badger algorithm (EHBA) is proposed to address the problem of easy convergence to local optima and difficulty in achieving fast convergence in the Honey Badger algorithm (HBA). The adoption of a dynamic opposite learning strategy broadens the search area of the population, enhances global search ability, and improves population diversity. In the honey harvesting stage of the honey badger (development), differential mutation strategies are combined, selectively introducing local quantum search strategies that enhance local search capabilities and improve population optimization accuracy, or introducing dynamic Laplacian crossover operators that can improve convergence speed, while reducing the odds of the HBA sinking into local optima. Through comparative experiments with other algorithms on the CEC2017, CEC2020, and CEC2022 test sets, and three engineering examples, EHBA has been verified to have good solving performance. From the comparative analysis of convergence graphs, box plots, and algorithm performance tests, it can be seen that compared with the other eight algorithms, EHBA has better results, significantly improving its optimization ability and convergence speed, and has good application prospects in the field of optimization problems.

Keywords: differential mutation operation; dynamic Laplace crossover; dynamic opposite learning strategy; honey badger algorithm; quantum local search.

Figure 1

Laplace density function curve.

Figure 2

Flowchart for the proposed EHBA optimization algorithm.

Figure 3

Convergence curves of EHBA and other algorithms on CEC2017 partial test functions.

Figure 3

Convergence curves of EHBA and other algorithms on CEC2017 partial test functions.

Figure 4

Box plots of EHBA and other algorithms on CEC2017 partial test functions.

Figure 4

Box plots of EHBA and other algorithms on CEC2017 partial test functions.

Figure 5

Convergence curves of incomplete algorithms on CEC2020.

Figure 6

Convergence curves of other HBA variant algorithms and EHBA on CEC2020.

Figure 7

Convergence curves of EHBA and other algorithms on CEC2020 partial test functions.

Figure 8

Box plots of EHBA and other algorithms on CEC2020 partial test functions.

Figure 9

Convergence curves of EHBA and other algorithms on CEC2022 partial test functions.

Figure 10

Box plots of EHBA and other algorithms on CEC2022 partial test functions.

Figure 11

Schematic view of welded beam problem.

Figure 12

The convergence curve diagram (Design problems of welded beam).

Figure 13

The convergence curve diagram (Vehicle side impact design).

Figure 14

The convergence curve diagram (Parameter estimation of frequency modulated sound waves).