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              <p>The minimum differential dispersion problem is a NP-hard&amp;nbsp;combinatorial optimization&amp;nbsp;problem with numerous relevant applications. In this paper, we propose an intensification-driven&amp;nbsp;tabu search algorithm&amp;nbsp;for solving this computationally challenging problem by integrating a constrained neighborhood, a solution-based tabu strategy, and an intensified search mechanism to create a search that effectively exploits the elements of intensification and diversification. We demonstrate the competitiveness of the proposed algorithm by presenting improved new best solutions for 127 out of 250&amp;nbsp;benchmark instances&amp;nbsp;(&amp;gt;50%). We study the search trajectory of the algorithm to shed light on its behavior and investigate the&amp;nbsp;spatial distribution&amp;nbsp;of high-quality solutions in the search space to motivate the design choice of the intensified search mechanism.</p>
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