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Subject: search.filters.subject.Constraint handling

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    Synthesis of four-bar linkage motion generation using optimization algorithms
    (2019-07-01)
    Phukaokaew, Wisanu
    ;
    Sleesongsom, Suwin
    ;
    Panagant, Natee
    ;
    Bureerat, Sujin
    Motion generation of a four-bar linkage is a type of mechanism synthesis that has a wide range of applications such as a pick-and-place operation in manufacturing. In this research, the use of meta-heuristics for motion generation of a four-bar linkage is demonstrated. Three problems of motion generation were posed as a constrained optimization probably using the weighted sum technique to handle two types of tracking errors. A simple penalty function technique was used to deal with design constraints while three meta-heuristics including differential evolution (DE), self-adaptive differential evolution (JADE) and teaching learning based optimization (TLBO) were employed to solve the problems. Comparative results and the effect of the constraint handling technique are illustrated and discussed.
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    A Comparative Study on Constraint Handling for Solving Economic Dispatch by Evolutionary Algorithms
    (2018-07-02)
    Nakawiro, Worawat
    Evolutionary algorithm (EA) has been well accepted as a suitable tool for solving non-convex economic dispatch problems. However the major challenge is how to handle both equality and inequality constraints properly. The penalized fitness is commonly used to evaluate quality of candidate solutions. Moreover searching for feasible space is very difficult for a problem with large number of variables and number of constraints. This paper proposes a general framework which can be applied to any EA for handling constraints in ED problems. Four EAs namely differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and harmony search (HS) were selected to demonstrate effectiveness in solving a non-convex ED problem of a 15 unit power system. Simulation results reveal that with the proposed constraint handling all optimization algorithms converge to the identical result based on 20 independent trials.
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    Four-bar linkage path generation through self-adaptive population size teaching-learning based optimization
    (2017-11-01)
    Sleesongsom, S.
    ;
    Bureerat, S.
    This paper proposes a new variant of teaching-learning based optimization (TLBO) for optimal path synthesis of a four-bar linkage. The new TLBO algorithm is called self-adaptive population size teaching-learning based optimization (SAP-TLBO) which is altered from the original version by integrating a self-adaptive strategy for population sizing. A new concept of path generation is designed for problems without prescribed timing, which is proposed to avoid the timing constraint. In the first stage, two traditional path generation test problems are used to test the proposed technique and the new optimizer. Then, the new optimizer is used to solve five mechanical design problems. The results show that the new design concept for path synthesis gives significantly better results than the classical exterior penalty function technique, whereas the computational time is increased. SAP-TLBO is significantly superior to its original version and other meta-heuristics.
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