  This study modifies the Covariance Matrix Adaptation Evolution Strategy
(CMA-ES) algorithm for multi-modal optimization problems. The enhancements
focus on addressing the challenges of multiple global minima, improving the
algorithm's ability to maintain diversity and explore complex fitness
landscapes. We incorporate niching strategies and dynamic adaptation mechanisms
to refine the algorithm's performance in identifying and optimizing multiple
global optima. The algorithm generates a population of candidate solutions by
sampling from a multivariate normal distribution centered around the current
mean vector, with the spread determined by the step size and covariance matrix.
Each solution's fitness is evaluated as a weighted sum of its contributions to
all global minima, maintaining population diversity and preventing premature
convergence. We implemented the algorithm on 8 tunable composite functions for
the GECCO 2024 Competition on Benchmarking Niching Methods for Multi-Modal
Optimization (MMO), adhering to the competition's benchmarking framework. The
results are presenting in many ways such as Peak Ratio, F1 score on various
dimensions. They demonstrate the algorithm's robustness and effectiveness in
handling both global optimization and MMO- specific challenges, providing a
comprehensive solution for complex multi-modal optimization problems.
