Wireless sensor networks require accurate target localization, often achieved
through received signal strength (RSS) localization estimation based on maximum
likelihood (ML). However, ML-based algorithms can suffer from issues such as
low diversity, slow convergence, and local optima, which can significantly
affect localization performance. In this paper, we propose a novel localization
algorithm that combines opposition-based learning (OBL) and simulated annealing
algorithm (SAA) to address these challenges. The algorithm begins by generating
an initial solution randomly, which serves as the starting point for the SAA.
Subsequently, OBL is employed to generate an opposing initial solution,
effectively providing an alternative initial solution. The SAA is then executed
independently on both the original and opposing initial solutions, optimizing
each towards a potential optimal solution. The final solution is selected as
the more effective of the two outcomes from the SAA, thereby reducing the
likelihood of the algorithm becoming trapped in local optima. Simulation
results indicate that the proposed algorithm consistently outperforms existing
algorithms in terms of localization accuracy, demonstrating the effectiveness
of our approach