A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

Flores-Pulido, Leticia; Portilla-Flores, Edgar Alfredo; Santiago-Valentín, Eric; Vega-Alvarado, Eduardo; Calva-Yáñez, María Barbara; Niño-Suárez, Paola Andrea

doi:10.1109/access.2020.3015942

Cited by 7 publications

(6 citation statements)

References 40 publications

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“…The first objective function, which is extensively studied for four-bar mechanis ms [26,[33][34][35][36]55], is the tracking error (TE) function:…”

Section: Objective Functionsmentioning

confidence: 99%

“…Besides meeting the Grashof criterion, the input link should also be the shortest link in order for a crankrocker four-bar mechanism to have a full rotation at its input link. Grashof criterion is a common constraint and frequently presented in four-bar linkage studies [26,34,35,55], but the cultivator design problem has some other constraints that are unique and introduced in this study.…”

Section: The Constraints Of Cultivator Design Problemmentioning

confidence: 99%

“…w i is the weight allocated to i th objective function by decision-maker where w i [0, 1] and Σ I i=1 w i = 1 and b i is a utopian value related to i th objective function and predefined by decision-maker. If p approaches ∞, the problem transforms into a ∞-norm problem and is called Chebyshev problem [53,54,59,60] or min-max method [54,55]. In that case, it is easy to prove that Equation ( 25) can be expressed as follows:…”

Section: Four-bar Mechanism Formulationmentioning

confidence: 99%

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Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control

et al. 2022

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Weeds compete with crops for water, nutrients, and light consequently, have adverse effects on the crop yield and overall productivity. Mechanical weeding is the most common non-chemical method for weed control, which is applied in organic farming, and the weed cultivator is the most common implement in mechanical weeding. This study aimed to design and develop an innovative active tool to optimize the cultivation depth, which can avoid damage to crop roots and improve the key performance indicators of an inter-row cultivator. A quasi-Newton optimization method and a hybrid of the non-dominated sorting genetic algorithm (NSGA-II) and goal attainment method were separately applied to synthesize and develop a four-bar mechanism for weeding requirements. The transmission angle of the mechanism and the desired path of the weeding blade were simultaneously optimized using these multi-objective optimization techniques. The performance of the developed four-bar cultivator based on the optimization techniques was compared with the ones developed based on the classic methods and also with several conventional tools evaluated in other studies. The results showed that applying the quasi-Newton optimization method and hybrid genetic algorithm can propose a more effective weed cultivator in terms of performance indicators, namely weeding performance, mechanical damage to crop plants and cultivation depth. In addition, the optimization of the transmission angle guaranteed the smooth rotations in the mechanism’s joints.

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“…The first objective function, which is extensively studied for four-bar mechanis ms [26,[33][34][35][36]55], is the tracking error (TE) function:…”

Section: Objective Functionsmentioning

confidence: 99%

Section: The Constraints Of Cultivator Design Problemmentioning

confidence: 99%

Section: Four-bar Mechanism Formulationmentioning

confidence: 99%

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Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control

et al. 2022

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“…They compared three hybrid strategies and verified that the cluster-based hybridization in DE (DEC) is the best hybrid approach. Flores-Pulido et al [70] proposed an improved harmony search (ImHS) algorithm to solve the dimensional synthesis problems of a four-bar path generator without prescribed timing. Zarkandi [71] proposed a population-based heuristic algorithm called a gravitational search algorithm (GSA) to determine multiple cognateand defect-free optimal solutions for the path synthesis problems of planar four-bar and slider-crank mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on the Synthesis of Path-Generating Four-Bar Linkages Using Metaheuristic Optimization Algorithms

Kang¹,

Jun²,

You³

2022

Applied Sciences

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Four-bar linkages are one of the most widely used mechanisms in industries. This paper presents a comparative study on the accuracy and efficiency of the optimum synthesis of path-generating four-bar linkages using five metaheuristic optimization algorithms. The utilized metaheuristic methods included two swarm intelligence-based algorithms, i.e., particle swarm optimization and hybrid particle swarm optimization, and three evolutionary-based algorithms, i.e., differential evolution, ensemble of parameters and mutation strategies in differential evolution, and linearly ensemble of parameters and mutation strategies in differential evolution. The objective function to be minimized is the sum of squares of the distance between the generated points and the precision points of a coupler point. The optimal design of four-bar linkages must meet the Grashof’s criteria and exhibit sequential constraints that can prevent the occurrence of order defect. This study investigated five representative cases of the dimensional synthesis of four-bar path generators with and without prescribed timing and compared the optimal solutions of the utilized five metaheuristic methods to those of previously reported algorithms in literature. The improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods. Moreover, three improved metaheuristic methods were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems.

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“…Then, they expanded new adaptive teaching-learning-based optimization (TLBO) to control parameter conformity. Flores-Pulido et al 37 introduced an Improvement Harmony Search (ImSH) to increase the metaheuristic algorithms search quality by bandwidth parameter modification. A comparison has then been made between traditional metaheuristics and their harmony search versions to illustrate the effectiveness of the introduced ImSH.…”

Section: Introductionmentioning

confidence: 99%

Geometrical Similarity Error Function-Innovative Adaptive Algorithm methodology in path generation synthesis of the four-bar mechanism using metaheuristic algorithms

Sardashti

Daniali

Varedi-Koulaei

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper presents a novel methodology for path generation synthesis of the four-bar mechanism. A new objective function for the path generation synthesis problem, namely, the Geometrical Similarity Error Function (GSEF), is introduced. Indeed, GSEF assesses the similarity between generated and desired paths, and its number of design variables is less than those in the other synthesis methods. Then, using an Innovative Adaptive Algorithm (IAA), some operators are utilized for matching two similar paths. GSEF-IAA methodology has some significant advantages over the reported synthesis methods. The method is fast, takes much less CPU time, and saves a large amount of computer memory. Four path generation problems are solved using GSEF-IAA, and the results are compared with those of previous methods using some well-known optimization algorithms to demonstrate the efficiency of GSEF-IAA methodology.

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A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

Cited by 7 publications

References 40 publications

Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control

Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control

Comparative Study on the Synthesis of Path-Generating Four-Bar Linkages Using Metaheuristic Optimization Algorithms

Geometrical Similarity Error Function-Innovative Adaptive Algorithm methodology in path generation synthesis of the four-bar mechanism using metaheuristic algorithms

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