Hybrid strategy enhanced crayfish optimization algorithm for breast cancer prediction

Abstract

Crayfish Optimization Algorithm (COA) suffers from degradation of diversity, insufficient exploratory capability, a propensity to become caught in local optima, and an imprecise search engine for optimization. To address these issues, the current research introduces a hybrid strategy enhanced crayfish optimization algorithm (MSCOA). Initially, a chaotic inverse exploration initialization method is utilized to establish the population's position with high diversity, significantly enhancing the global exploration capability. Second, an adaptive t-distributed feeding strategy was employed to define the connection between feeding behavior and temperature, increasing population variety and enhanced the algorithm's local search effectiveness. Finally, an adaptive ternary optimization mechanism is introduced in the exploration phase: a curve growth acceleration factor is used to collaboratively guide global and individual optimal information, while a hybrid adaptive cosine exponential weigh dynamically adjusts the search intensity. Additionally, an inverse worst individual variant reinforcement approach is employed to enhance the population evolution efficiency. In the hybrid test sets of CEC2005 and CEC2019, MSCOA shows improved convergence accuracy compared to the traditional COA algorithm, and the Wilcoxon test (p < 0.05) confirms its superiority over five other comparison algorithms. MSCOA outperforms other algorithms in terms of robustness, convergence speed, and solution accuracy, although there is still room for further improvement. When combined with Extreme Learning Machine (ELM) and applied to the Wisconsin breast cancer dataset, the MSCOA-ELM model achieved 100% accuracy and F1 score, a 28.9% improvement over the baseline ELM, demonstrating the algorithm's efficiency and generalization ability in solving practical optimization problems.

Keywords: Cancer prediction; Chaotic initialization; Crayfish optimization algorithm; ELM; T-distribution feeding strategy; Ternary optimization mechanism.

Fig. 1

Structure diagram of crayfish.

Fig. 2

Three kinds of chaotic mapping histograms.

Fig. 3

Flow chart of chaotic reverse initialization strategy.

Fig. 4

Image comparison of T-distribution, Cauchy distribution, Gaussian distribution.

Fig. 5

Image comparison of T-distribution, Cauchy distribution, Gaussian distribution.

Fig. 6

Image of parameters a, b selected.

Fig. 7

Image of .

Fig. 8

Flow chart of MSCOA.

Fig. 9

Convergence curves of various algorithms.

Fig. 9

Convergence curves of various algorithms.

Fig. 9

Convergence curves of various algorithms.

Fig. 10

Convergence diagram of various algorithms.

Fig. 10

Convergence diagram of various algorithms.

Fig. 10

Convergence diagram of various algorithms.

Fig. 10

Convergence diagram of various algorithms.

Fig. 10

Convergence diagram of various algorithms.

Fig. 10

Convergence diagram of various algorithms.

Fig. 11

The box-plot of various algorithms.

Fig. 12

Algorithms performance comparison across different problem sizes.

Fig. 13

ELM model structure.

Fig. 14

Breast cancer prediction process based on MSCOA-ELM.

Fig. 15

Binary confusion matrix.

Fig. 16

Box Plot of Optimal Accuracy Distribution and Optimal Hidden Neuron Number Distribution.

Fig. 17

Comparison of ROC for Different Models on Diabetes Dataset.

Fig. 18

Comparison of ROC Curves for Different Models on Breast Cancer Dataset.

Fig. 18

Comparison of ROC Curves for Different Models on Breast Cancer Dataset.

Fig. 18

Comparison of ROC Curves for Different Models on Breast Cancer Dataset.

Fig. 19

Comparison of ROC Curves for Different Models on Breast Cancer Dataset.