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The urban heat effect brings with a series of challenges for urban ecology and population health. In order to mitigate the adverse impacts of the urban heat effect quantitatively and effectively, a new land use search and exchange strategy is proposed to improve the multi-type ant colony algorithm in this paper and applied to a case study of Kunming, China. The results reveal the following: (1) the optimization algorithm can quickly locate the high-temperature area of the city and optimize the land use composition of this area; (2) the mean surface temperature of the core area can be reduced by 0.43–0.85 °C after land use optimization; and (3) Compared with Pareto optimization strategy and particle swarm optimization algorithm, the method proposed in this paper can achieve better optimization effect, higher computing efficiency and stability. This method can provide an effective scientific basis for sustainable urban planning and design, urban ecology and livability, and other urban retrofits. • The improved MACO algorithm can obtain high similiarity in fewer iterations. • The improved MACO algorithm can quickly locate the high-temperature area. • The improved MACO algorithm outperforms the Pareto strategy and PSO algorithm. [ABSTRACT FROM AUTHOR]