Multimodal optimization requires finding many optima rather than merely keeping a diverse population. Yet most niching-based evolutionary algorithms rely on distances or density estimators without explicitly recovering the underlying peak--basin organization in the decision space, which can lead to pseudo-multimodality: many distinct individuals ultimately collapse into only a few basins. We introduce Chaotic Landscape-Decoding Evolution (CLDE), a decision-space-centric framework that turns multimodal search into a closed loop of decode--value--allocate--refine. CLDE injects controlled global exploration via a logistic chaotic map with a decaying step size, then builds a $k$-nearest-neighbor graph on a decoding canvas and performs persistence-guided basin growing that merges peaks only when they are not separated by deep valleys. An adaptive persistence threshold continuously tunes the decoding resolution online to avoid over-fragmentation and over-merging. Guided by the decoded structure, CLDE carries out basin-wise selection and refinement to improve solution quality while preserving basin coverage. We instantiate CLDE as CLDE-S and CLDE-M for single- and multi-objective multimodal optimization. Experiments on 20 CEC2013 functions show that CLDE-S achieves strong peak ratio under the same evaluation budget, while on DTLZ and MMMOP suites CLDE-M attains competitive IGD/IGDx, with pronounced gains on strongly multimodal problems.

翻译：多模态优化需要找到多个最优解，而不仅仅是保持种群的多样性。然而，大多数基于小生境的进化算法依赖于距离或密度估计，并未显式恢复决策空间中的峰值-盆地结构，这可能导致伪多模态现象：许多不同的个体最终坍缩到仅少数几个盆地中。我们提出混沌景观解码进化（CLDE），一种以决策空间为中心的框架，将多模态搜索转化为解码-评估-分配-精化的闭环过程。CLDE通过带衰减步长的逻辑混沌映射注入受控的全局探索，然后在解码画布上构建k近邻图，并执行持久性引导的盆地生长，仅当峰值未被深谷隔开时才合并它们。自适应持久性阈值在线连续调节解码分辨率，以避免过度碎片化和过度合并。在解码结构引导下，CLDE进行基于盆地的选择与精化，以改善解质量同时保持盆地覆盖。我们将CLDE实例化为CLDE-S和CLDE-M，分别用于单目标和多目标多模态优化。在20个CEC2013函数上的实验表明，CLDE-S在相同评价预算下实现了强峰值比率；而在DTLZ和MMMOP测试套件上，CLDE-M获得了具有竞争力的IGD/IGDx指标，在强多模态问题上优势尤为显著。