The　Rafflesia　Optimization　Algorithm　(ROA)　is　an　optimization　algorithm　that　mimics　the　growth　cycle　of　the　Rafflesia.　Building　upon　the　ROA,　this　study　introduces　a　novel　heuristic　algorithm　called　Orthogonal　Learning　Quasi-Affine　Transformation　Evolutionary　Rafflesia　Optimization　Algorithm　(OLQROA).　The　QUATRE　method　and　the　Orthogonal　Learning　approach　are　combined　in　the　OLQROA　algorithm.　Compare　OLQROA　with　ROA　algorithm,　improved　ROA　algorithms,　and　other　three　mature　algorithms　using　CEC2017　benchmark　function.　The　outcomes　of　the　experiments　show　that　OLQROA　works　better　than　the　aforementioned　algorithms.　Additionally,　OLQROA　is　used　to　apply　three-dimensional　wireless　sensor　coverage,　producing　superior　results　in　comparison　to　the　aforementioned　algorithms.