AMRABC_TS Algorithm: An adaptive dimension modification rate artificial bee colony algorithm with two search strategies

This repository contains the official and complete implementation of the AMRABC_Ts algorithm (Adaptive dimension Modification Rate Artificial Bee Colony algorithm with Two search strategies), a novel swarm intelligence optimization method proposed in the research paper "An adaptive dimension modification rate artificial bee colony algorithm with two search strategies" (submitted to Mathematics, MDPI). The AMRABC_Ts algorithm is designed to address the limitations of the standard Artificial Bee Colony (ABC) algorithm, such as slow convergence and poor balance between exploration and exploitation. It integrates two key innovations: (1) dual search strategies (exploration-oriented and exploitation-oriented) with dynamic selection based on real-time search performance feedback; (2) an adaptive dimension modification rate mechanism that adjusts the probability of multi-dimensional updates according to the success rate of previous iterations. The code includes full implementations of 22 standard benchmark functions (f1–f22 from the DFS dataset), covering unimodal, multimodal, and special-type functions, with configurable problem dimensions (30D, 50D, 100D) to support diverse optimization scenario tests. It automatically performs statistical analysis (mean, standard deviation, success rate, convergence speed) across 25 independent runs, generates detailed convergence curves (single-run and averaged), and outputs machine-readable results for easy post-processing (compatible with Python, MATLAB, Excel). All experiments in the associated research paper can be fully reproduced using this code, with clear parameter configuration, compilation instructions, and output interpretation. The implementation relies only on standard C++ libraries (no external dependencies) and supports cross-platform execution (Windows, Linux, macOS). This repository serves as a reliable resource for researchers in swarm intelligence, numerical optimization, and related fields to verify, extend, or compare with the AMRABC_Ts algorithm.