Multiple Objective Social Group Optimization for Time–Cost–Quality–Carbon Dioxide in Generalized Construction Projects

This study presents a novel approach named as “multiple objective social group optimization” (MOSGO) to tradeoff time, cost, quality, and carbon dioxide emission (TCQC) factors in generalized construction projects. The proposed algorithm modifies the operation mechanism to balance the exploration and exploitation abilities of the optimization process. The TCQC tradeoff problem considers all types of logical relationships between project activities. Two practical case studies demonstrate the ability of MOSGO-generated, non-dominated solutions. In addition, evidential reasoning is applied to select a compromise solution for project implementation. Comparisons between the MOSGO and four well-known algorithms (MODE, MOABC, MOPSO, and NSGA-II) to verify the efficiency and effectiveness of the developed algorithm. According to the statistical analysis, the proposed algorithm generated the highest values of diversification measurement (DM) of 26.113 and 40.27; the highest values of hyper-volume (HV) of 0.875 and 0.881 in case 1 and case 2, respectively. The proposed algorithm also found solutions with lowest mean ideal distance (MID) and spread (SP) values of 0.872 and 0.462 in the first case and of 0.754 and 0.689 in the second case. MOSGO showed had better diversify and convergence, gained wider spread, and yielded higher uniformity of solutions than the compared multiple objective evolutionary algorithms.