A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

Abstract

This paper proposes a novel hybrid teaching-learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teachinglearning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after the teacher phase in TLBO with the best half of the population obtained after PSO. The population so obtained is used subsequently in learner phase of TLBO. To validate the proposed algorithm, five constrained benchmark functions are considered to prove its robustness and efficiency. The proposed algorithm is applied to synthesize four-bar linkage for prescribed path. It is found that the HTLPSO performs better than other single nature-inspired algorithms for path synthesis problem in mechanism theory. Hence, HTLPSO may prove to be an important tool for mechanism design to follow the prescribed path.

Design and development of vibratory cultivator using optimization algorithms

“…16, which indicates that the results obtained through HTLPSO are better with minimum error. TLBO results also converge to the best solution, but regarding error and number of iterations, HTLPSO performed well and is in support of the previous work done [46]. The error in HTLPSO was found to be 0.73948, and in TLBO it is 0.82115, respectively.…”

“…Where N is the no of precision points. θ 3 and θ 4 can be calculated through loop closure equations of four bar mechanism [34,35,46]. r 1 , r 2 , r 3 , r 4 , ly, lx, Xo, Yo are in mm.…”

“…Another algorithm teaching learning based (TLBO) is a parameter less algorithm which uses the philosophy of teaching and learning based concept [42]. There are some improved algorithms [43][44][45] proposed recently by named as Modified Particle Search Algorithm and Hybrid teaching-learning based algorithm proposed by [46] combination of TLBO and PSO to solve and identify the design variables for the problem. For any algorithm, there are two phases one is exploration and exploitation.…”

“…HTLPSO algorithm technique [46] is used for solving our objective function to find the optimum solution in a lesser number of functional evaluations. Flow chart describes the HTLPSO technique in Fig.…”

Optimal design and analysis of oscillatory mechanism for agricultural tillage operation

“…Variety of nature-inspired algorithms are there, such as genetic algorithm (GA) [17,18], particle swarm optimization (PSO) [19], teaching learning-based algorithm (TLBO) [20], whale optimization algorithm (WOA) [21] and many more, to solve the objective function which is a Euclidean distance [22] in our case that is selected path. Improved algorithms such as modified particle search algorithm (MPSO) [23] and hybrid teaching learning-based algorithm [24] are proposed for more refinement in the solution of the design variables. Thus, in this paper, an optimal fourbar crank-rocker mechanism is proposed for agricultural tillage operation for the desired path.…”