Finding Pareto Solution Based on Hybrid Slime Mold Algorithm with Tournament Selection for Solving Multiple-Objectives Optimization in Construction Projects

Artificial intelligence (AI) is being utilized in the construction industry to address optimization challenges and to promote the industrial sector's development and commercialization. The Adaptive Selection Slime Mold Algorithm (ASSMA) was developed in recent years to tackle the problem of optimizing the three aspects of time, cost, and quality in construction projects by combining the slime mold algorithm (SMA) with Tournament Selection (TS). With ASSMA's help, the transition from random selection to selecting the greatest candidate to decide the best outcome will be hastened. Furthermore, to improve its superiority and efficiency, the ASSMA model will be compared to other common algorithms such as Multi-Objective Particle Swarm Optimization (MOPSO), Opposition-based Multi-Objective Differential Evolution (OMODE), Multi-Objective Differential Evolution (MODE), and non-dominant sorting genetic algorithm II (NSGA-II). According to the overall findings, the ASSMA model displays diversity and gives a powerful and persuasive optimum solution enabling readers to see the future potential of the proposed approach.