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Cyclic-system-based optimization (CSBO) is an innovative metaheuristic algorithm (MHA) that draws inspiration from the workings of the human blood circulatory system. However, CSBO still faces challenges in solving complex optimization problems, including limited convergence speed and a propensity to get trapped in local optima. To improve the performance of CSBO further, this paper proposes improved cyclic-system-based optimization (ICSBO). First, in venous blood circulation, an adaptive parameter that changes with evolution is introduced to improve the balance between convergence and diversity in this stage and enhance the exploration of search space. Second, the simplex method strategy is incorporated into the systemic and pulmonary circulations, which improves the update formulas. A learning strategy aimed at the optimal individual, combined with a straightforward opposition-based learning approach, is employed to enhance population convergence while preserving diversity. Finally, a novel external archive utilizing a diversity supplementation mechanism is introduced to enhance population diversity, maximize the use of superior genes, and lower the risk of the population being trapped in local optima. Testing on the CEC2017 benchmark set shows that compared with the original CSBO and eight other outstanding MHAs, ICSBO demonstrates remarkable advantages in convergence speed, convergence precision, and stability.