Optimal design of four-bar mechanisms using a hybrid multi-objective GA with adaptive local search

Responding to an increasing demand for mechanism synthesis tools that are both efficient and accurate, this paper presents a novel approach to the multi-objective optimal design of four-bar linkages for path-generation purposes. Three, often conflicting criteria including the mechanism's tracking error, deviation of its transmission angle from 90° and its maximum angular velocity ratio are considered as objectives of the optimization problem. To accelerate the search in the highly multimodal solution space, a hybrid Pareto genetic algorithm with a built-in adaptive local search is employed which extends its exploration to an adaptively adjusted neighborhood of promising points. The efficiency of the proposed algorithm is demonstrated by applying it to a classical design problem for one, two and three objective functions and comparing the results with those reported in the literature. The comparison shows that the proposed algorithm distinctly outperforms other algorithms both quantitatively and qualitatively (from a practical point of view).

► Mechanism synthesis/design is best performed when formulated as a multi-objective optimization problem.
► "Tracking error" and "deviation of the transmission angle from 90°" are important performance measures of 4-bar mechanisms.
► Performance of 4-bar mechanisms will be improved if "maximum angular velocity ratio" is included in the objectives sets.
► A well developed set of Pareto optimal solutions clearly demonstrate the possible independence of individual objectives.
► The "Pareto GA with Adaptive Local Search (PGAALS)" is capable of generating well developed Pareto sets quite efficiently.