The main challenge of multimodal optimization problems is identifying multiple peaks with high accuracy in multidimensional search spaces with irregular landscapes. This work proposes the Multiple Global Peaks Big Bang-Big Crunch (MGP-BBBC) algorithm, which addresses the challenge of multimodal optimization problems by introducing a specialized mechanism for each operator. The algorithm expands the Big Bang-Big Crunch algorithm, a state-of-the-art metaheuristic inspired by the universe's evolution. Specifically, MGP-BBBC groups the best individuals of the population into cluster-based centers of mass and then expands them with a progressively lower disturbance to guarantee convergence. During this process, it (i) applies a distance-based filtering to remove unnecessary elites such that the ones on smaller peaks are not lost, (ii) promotes isolated individuals based on their niche count after clustering, and (iii) balances exploration and exploitation during offspring generation to target specific accuracy levels. Experimental results on twenty multimodal benchmark test functions show that MGP-BBBC generally performs better or competitively with respect to other state-of-the-art multimodal optimizers.

翻译：多模态优化问题的主要挑战在于，在具有不规则景观的多维搜索空间中，高精度地识别多个峰值。本文提出了多全局峰值大爆炸-大坍缩（MGP-BBBC）算法，该算法通过为每个算子引入专门机制，应对多模态优化问题的挑战。该算法扩展了大爆炸-大坍缩算法——一种受宇宙演化启发的最先进元启发式算法。具体而言，MGP-BBBC将种群中的最佳个体分组为基于聚类的质心，然后以逐渐降低的扰动对其进行扩展，以确保收敛性。在此过程中，它（i）应用基于距离的过滤以移除不必要的精英个体，从而避免较小峰值上的个体丢失；（ii）在聚类后根据其小生境计数提升孤立个体；（iii）在子代生成过程中平衡探索与利用，以瞄准特定的精度水平。在二十个多模态基准测试函数上的实验结果表明，相较于其他最先进的多模态优化器，MGP-BBBC通常表现更优或具有竞争力。