Pareto Front Case Study
7. Pareto Optimality Majority real world problems are having more than one objective functions. Minimum two to three objective functions and they are also in conflicting nature. For example, if a company needs to earn more profit means it has to cut down the manufacturing cost without compromising the product quality. In general, if the product quality high means, the manufacturing cost is high. But the need is the company has to reduce the manufacturing cost without reducing the product quality. Practically it is not possible to reduce the manufacturing cost beyond certain level without compromising the quality. No improvement can be done in one objective function without affecting another objective function. i.e. Only by minimizing the quality, the production cost can be reduced. To handle this situation, multi objective optimization algorithms offer a Pareto front to the user instead of suggesting a single solution. The Pareto front has more number of non dominating solutions. Non dominating solutions mean that one solution cannot be said as superior or inferior to another solution. So by giving preference (weightage to objective functions) only the user can say one solution is better to another solution. The set of optimal solutions with …show more content…
From the results, the following points have been observed: (1) For all optimal solutions in the pareto fronts gave by both MOPSO and MODE, the robot joints displacements, velocities, accelerations, jerks and actuator torques are well within the limits. Also they satisfy the obstacle avoidance criterion. So the resulted trajectories are feasible and practically possible. (2) Regarding convergence, MOPSO converges quickly within fewer generations compared to MODE. Also MOPSO algorithm running time is less than MODE algorithm. So MOPSO is faster than MODE. (3) But MODE algorithm gives more number of optimal solutions in pareto-optimal front than that of
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