Construction project managers aspire to achieve projects of the highest quality while minimizing time and cost. Presently, most trade-off analysis models focus predominantly on the traditional trifecta of time, cost, and quality, often overlooking other pivotal factors and project traits. This study introduces the Chaotic Adaptive Multi-Objective Sea Horse (CAMOSH), an innovative algorithm crafted to navigate the nuanced tradeoffs among time, cost, quality, risk, and environment (TCQRE) in comprehensive construction projects. CAMOSH leverages chaos sequences for population initialization and integrates an adaptive selection technique to harmonize exploitation and exploration during optimization. To facilitate the early-stage project implementation, the multiple-criteria decision analysis (MCDA) method is employed. When benchmarked against six esteemed algorithms across two construction projects, CAMOSH shows its superiority. Statistical evaluation underscores CAMOSH’s outstanding performance, evidenced by its top-ranking values in Diversity Measure (DM) and Hyper-Volume (HV) for both case studies. Furthermore, the algorithm consistently yields solutions with the most optimal Spacing Metric (SM) values and operates in the shortest time frames. These results collectively validate CAMOSH as an instrumental resource, equipping project managers to adeptly balance multifaceted construction project objectives and pinpoint the most effective project timeline.
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