IBMSMA: An Indicator-based Multi-swarm Slime Mould Algorithm for Multi-objective Truss Optimization Problems

Advances in Slime Mould Algorithm: A Comprehensive Survey

“…Li YiFei et al [43] embedded polynomial chaos expansion (PCE) into the SMA for multiparameter identification of concrete dams. Yin Shihong et al [44] organized a set of chaotic sequences using a chaotic grouping mechanism (CGM) to improve the population diversity results. Xuebing Cai et al [45] proposed a multi-objective SMA, called CRFSMA, which incorporated a chaotic mechanism to increase its search ability.…”

“…Son, P. V. H. et al [108] proposed an AOSMA to find solutions for a construction project's multi-objective optimization problem. Yin Shihong et al [44] presented a multi-objective SMA (IBMSMA) and applied it to the multi-objective truss optimization problem. Cai Xuebing et al [45] also proposed an MOSMA, and the simulation test demonstrated that the MOSMA attained the best convergence, accuracy, and diversity results among the compared multi-objective algorithms.…”

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

“… Indicator-based MOEAs. These MOEAs utilize performance metrics related to solution quality as selection standards, guiding the search towards consistent enhancement of the overall population's anticipated attributes 36 , 37 . IBEA, a pioneering indicator-based MOEA, was developed by Zitzler and Kunzl 38 .…”

“…To gauge the potency of this proposed method, we employ distinct benchmark test functions: ZDT 65 , DTLZ 66 , Constraint 67 , 68 (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design Brushless DC wheel motor 69 (RWMOP1), Helical spring 68 (RWMOP2), Two-bar truss 68 (RWMOP3), Welded beam 70 (RWMOP4), Disk brake 71 (RWMOP5). The objective of this assessment is to compare the efficacy of our proposed method against MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO, using metrics like generational distance (GD) 34 , inverse generational distance (IGD) 35 , hypervolume 36 , Spacing 37 , Spread 36 and run time (RT). The approximations of the Pareto-front produced by our method are evaluated using these metrics.…”

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

“…We aimed to gauge their capabilities in swiftly converging to the true Pareto optimal front and the distribution of the obtained non-dominated solutions. Upon assessing their convergence and coverage using MO metrics and the produced Pareto optimal fronts on benchmark suites (ZDT [ 54 ], DTLZ [ 54 ], Constraint [ 68 , 69 ] and engineering design problems [ 55 , 56 ]), we discerned that these algorithms still exhibited shortcomings in convergence and coverage using metrics like generational distance (GD) [ 70 ], inverse generational distance (IGD) [ 71 ], hypervolume [ 72 ], Spacing [ 73 ], Spread [ 72 ] and run time (RT). The approximations of the Pareto-front produced by our method are evaluated using these metrics.…”