Multi-objective optimization approach for Inverse Kinematics of redundant manipulator for welding applications

Reddy, A. Sridhar; Chembuly, V. V. M. J. Satish; Rao, V.V.S. Kesava

doi:10.1016/j.matpr.2023.04.571

Cited by 2 publications

(1 citation statement)

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“…In the recent decades, there is a growing interest in the design of optimization algorithms and their applications in the fields of sciences and technologies (1)(2)(3)(4). In this way, many optimization techniques have been presented to solve various real-world problems by researchers in the recent years (5)(6)(7).…”

Section: Introductionmentioning

confidence: 99%

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

Mahmoodabadi,

Nemati,

Danesh

2024

IJE

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To enhance the performance of meta-heuristic algorithms, the development of new operators and the efficient combination of various optimization techniques are valuable strategies for discovering global optimal solutions. In this research endeavor, we introduce a novel optimization algorithm called PGS (Particle Swarm Optimization-GA-Sliding Surface). PGS combines the strengths of particle swarm optimization (PSO), genetic algorithm (GA), and sliding surface (SS) to tackle both mathematical test functions and real-world optimization problems. To achieve this, we adaptively tune the weighting function and learning coefficients of the PSO algorithm using the sliding mode control's SS relation. The global best particle discovered through the PSO method serves as one of the parents in the GA's crossover operation. This new crossover operator is then probabilistically integrated with an improved particle swarm optimization algorithm, enhancing convergence speed and facilitating escape from local optima. We evaluate the proposed algorithm's performance on both uni-modal and multi-modal mathematical test functions, considering un-rotated and rotated cases, thereby testing its effectiveness and efficiency against other prominent optimization techniques. Furthermore, we successfully implement the PGS algorithm in optimizing the state feedback controller for a nonlinear quadcopter system and determining the cross-section for an inelastic compression member.

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