This paper proposes a novel metaheuristic-based optimization technique called search space reduction (SSR) optimization algorithm. This algorithm attempts to solve the common pitfalls in the existing algorithms in the literature by randomly generating the search agents in every iteration instead of following the best solution. This new algorithm is simple, computationally efficient, which is based on the concept of reducing the search space. The performance of this algorithm is tested over classical test functions and CEC’17 benchmark test functions. The results are compared with well-established algorithms in the literature. The test results show that the proposed algorithm exhibits good exploration and exploitation capabilities. Further, this algorithm also outperforms other algorithms in solving multimodal optimization problems. In addition to this, the computational complexity of this algorithm is also presented according to CEC’17 guidelines. The proposed algorithm is also employed to solve three engineering design problems and a more complex re-entry trajectory optimization problem to show its effectiveness.

本文提出了一种新的基于元启发式的优化技术，称为搜索空间缩减（SSR）优化算法。该算法尝试通过在每次迭代中随机生成搜索代理而不是遵循最佳解决方案来解决文献中现有算法中的常见陷阱。该新算法基于减少搜索空间的概念，操作简单，计算效率高。该算法的性能在经典测试功能和CEC'17基准测试功能上进行了测试。将结果与文献中公认的算法进行比较。测试结果表明，该算法具有良好的探索和开发能力。此外，该算法在解决多峰优化问题方面也胜过其他算法。除此之外，还根据CEC'17指南介绍了该算法的计算复杂性。该算法还被用来解决三个工程设计问题和一个更复杂的重入轨迹优化问题，以证明其有效性。