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Abstract
Gear Train Design problem is most important design problem for machine tools manufacturers. Recent work on gear train improvement has been bound towards multi-shaft gear trains of the speed-change kind, where major focus is to maximize the range of operating speeds and to minimize the number of gears and spindles. In the proposed research, a hybrid meta-heuristic search algorithm is presented to design and optimize multi-spindle gear trains problem. The objective of the research is to optimize gear trains on the basis of minimum overall centre distance, minimum overall size, minimum gear volume, or other desirable criteria, such as maximum contact or overlap ratios. The proposed hybrid meta-heuristic search algorithm is inspired by canis lupus family of grey wolves and exploitation capability of existing grey wolf optimizer is further enhanced by pattern search algorithm, which is a derivative-free, direct search optimization algorithm suitable for non-differential, discontinuous search space and does not require gradient for numerical optimization problem and have good exploitation capability in local search space. The effectiveness of the proposed algorithm has been tested on various mechanical and civil design problem including gear train design problem, which includes four different gear and experimental results are compared with others recently reported heuristics and meta-heuristics search algorithm. It has been found that the proposed algorithm indorses its effectiveness in the field of nature inspired meta heuristics algorithms for engineering design problems for hybrid electric vehicles.
Highlights
Multidisciplinary design optimization and multidisciplinary system design optimization are emerging area for the solution of design and optimization problems incorporating a number of disciplines
While evaluating the optimization problem exploration and exploitation are the criteria to be taken into account based on these two features the algorithms are classified into two categories consisting of population based search algorithm which is exploration oriented and the other one is evolution based algorithms which are exploitation focused and there should be a good balance between them so as to enhance the working efficiency of the resultant algorithm
Various meta-heuristics search algorithms has been implemented such as Biogeography based Optimizer [1], Grey Wolf Optimizer [2], Ant Lion Optimizer [3], Moth Flame Optimizer [4], Multi Verse Optimizer [5], Dragon Fly Algorithm [6],Sine Cosine Algorithm [7],Lightning Search Algorithm [8], Seeker Optimization Algorithm [9],Virus Colony Search Algorithm [10], Whale Optimization Algorithm [11], Wind Driven Optimization [12]
Summary
Multidisciplinary design optimization and multidisciplinary system design optimization are emerging area for the solution of design and optimization problems incorporating a number of disciplines. Optimization techniques are considered to be one of the best tool for solving the engineering problems and to find the optimal results for the problem. These approaches consider the problem as black box and find the optimal solution. A few traditional techniques are accessible to take care of the unit commitment issue Be that as it may, every one of these strategies require the correct numerical model of the framework and there is a shot of stalling out at the nearby optima. The No-Free-Lunch theorem for optimization allow developers to develop new algorithm or to improve the existing algorithm because, it logically proves that there is no such optimization algorithm which can solve all the optimization problems with equal efficiency for all. There is always a scope or improvement to develop the algorithm which could work well for most of the problems
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