Are the Several soft computing algorithms for instance PSO, GA and other related techniques better than Goal Programming and Fuzzy Goal Programming for solving the multiple objective functions? Since Several journals rejecting paper with the reason that authors didn't use soft computing algorithm to solve the formulated model. If the the formulated model is multiple linear objective functions subject to the linear set of constraints. Which techniques is better Soft computing algorithm or goal programming approachs?
The effectiveness and/or superiority of GA and PSO depends very much on the structure of the problem and the results obtained will definitely be better than goal programming in almost all cases. For multiple objectives, NSGA-II, SPEA2, GrEA etc. give better result.
Please give any reference study wherein it has been proved. Based upon which we can conclude that the soft computing GA and PSO and other related techniques are better than standard multiple objective techniques like Goal and Fuzzy Goal Programming. I am very much interested to know the superiority of soft computing over standard techniques. Also discuss the nature of state wherein soft computing provide good solution then standard methods.
Sir,
Please see the paper entitled "Solving goal programming problems using multi-objective genetic algorithms " by Kalyanmoy Deb.
K. Deb. Solving goal programming problems using multi-objective genetic algorithms. In Proceedings of Congress on Evolutionary Computation, pages 77--84, 1999.]]
As per my information the soft computing algorithm provides better results than standard algorithm if the feasible space is nonconvex. But my questions about the case where in objective functions and constraints are linear in nature. Moreover, if problem is non linear in nature but the feasible space is convex then what be the best algorithms.