Performance Evaluation of Particle Swarm Optimization Algorithm for Optimal Design of Belt Pulley System

Journal of Applied Mathematics

Saravanan³

2015

Self Cite

The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

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Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

Journal of Applied Mathematics

Saravanan³

2015

Self Cite

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“…A detailed study regarding the design, modeling, structural analysis and manufacturing of helical gear in marine engines has been reported in Venkatesh et al [3]. Sabarinath et al [4][5][6] used two important metaheuristic algorithms namely Particle swarm optimization (PSO) and Differential Evolution (DE) for minimizing weight of a belt pulley system [4], minimizing the power loss of worm gear mechanism [5] and minimizing weight of a helical compression spring [6]. Geem et al [7] introduced harmony search (HS) which is based on the music improvisation process of musicians in the hunt for a magnificent harmony.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Design Optimization of Helical Gear Pair Using Adaptive Parameter Harmony Search Algorithm

Jeganathan³

et al. 2015

AMM

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Mechanical design engineers design products by selecting the best possible materials and geometries that satisfies the specific operational requirements of the design. It involves lot of creativity and aesthetics to make better designs. A gear design makes the designer to compromise many design variables so as to arrive the best performance of a gear set. The best possible way for multi variable, Multiobjective gear design is to try design optimization. For many complex engineering optimization problems multi objective design optimization methods are used to simplify the design problem. In this paper, multiobjective design of helical gear pair transmission with objective functions namely volume of the small and large helical gear and opposite number of overlap ratio is taken into account. The design variables considered are normal module, helix angle, gear width coefficient and teeth number of small helical gear. A recent meta-heuristic algorithm namely parameter adaptive harmony search algorithm is applied to solve this problem using the weighted sum approach. It is evident from the results that the proposed approach is performing better than other algorithms.

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Performance Evaluation of Differential Evolution and Particle Swarm Optimization Algorithms for Optimizing Power Loss in a Worm Gear Mechanism

Lecture Notes in Electrical Engineering

Saravanan³

2014