This paper proposes an improved variant of the differential evolution (DE) algorithm, specifically the DE/target-to-best/I scheme, by incorporating a neighborhood-based mutation operator. The proposed method aims to balance exploration and exploitation capabilities, addressing the issues of slow convergence and premature convergence in traditional DE algorithms. The neighborhood-based mutation operator draws inspiration from particle swarm optimization (PSO), where each population member's neighborhood is defined over the index graph of parameter vectors. This approach allows for a more dynamic and adaptive exploration of the search space, enhancing the algorithm's performance on both benchmark and real-world optimization problems. The paper also investigates the application of these new DE variants to real-life problems, such as parameter estimation for frequency-modulated sound waves and spread spectrum radar poly-phase code design. Experimental results demonstrate that the proposed variants outperform or at least match the performance of several existing DE variants and other evolutionary algorithms over a suite of 24 benchmark functions.This paper proposes an improved variant of the differential evolution (DE) algorithm, specifically the DE/target-to-best/I scheme, by incorporating a neighborhood-based mutation operator. The proposed method aims to balance exploration and exploitation capabilities, addressing the issues of slow convergence and premature convergence in traditional DE algorithms. The neighborhood-based mutation operator draws inspiration from particle swarm optimization (PSO), where each population member's neighborhood is defined over the index graph of parameter vectors. This approach allows for a more dynamic and adaptive exploration of the search space, enhancing the algorithm's performance on both benchmark and real-world optimization problems. The paper also investigates the application of these new DE variants to real-life problems, such as parameter estimation for frequency-modulated sound waves and spread spectrum radar poly-phase code design. Experimental results demonstrate that the proposed variants outperform or at least match the performance of several existing DE variants and other evolutionary algorithms over a suite of 24 benchmark functions.