The Harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components

2019 ◽  
Vol 61 (8) ◽  
pp. 744-748 ◽  
Author(s):  
Betül Sultan Yıldız ◽  
Ali Rıza Yıldız
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Structural Design ◽  
Dragonfly Algorithm ◽  
Salp Swarm Algorithm ◽  
Grasshopper Optimization Algorithm ◽  
Structural Design Optimization ◽  
Vehicle Components ◽  
Swarm Algorithm ◽  
Grasshopper Optimization

A new Hybrid Taguchi-salp swarm optimization algorithm for the robust design of real-world engineering problems

2021 ◽  
Vol 63 (2) ◽  
pp. 157-162
Author(s):  
Ali Rıza Yıldız ◽  
Mehmet Umut Erdaş
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Structural Design ◽  
Design Problem ◽  
Optimization Techniques ◽  
Stress Constraint ◽  
Element Analysis ◽  
Salp Swarm Algorithm ◽  
Grasshopper Optimization Algorithm ◽  
Swarm Algorithm

Abstract In this paper, a new hybrid Taguchi salp swarm algorithm (HTSSA) has been developed to speed up the optimization processes of structural design problems in industry and to approach a global optimum solution. The design problem is posed for the shape optimization of a seat bracket with a mass objective function and a stress constraint. Objective function evaluations are based on finite element analysis, while the response surface method is used to obtain the equations necessary for objective and constraint functions. Recent optimization techniques such as the salp swarm algorithm, grasshopper optimization algorithm and, Harris hawks optimization algorithm are used to compare the performance of the HTSSA in solving the structural design problem. The results show the hybrid Taguchi salp swarm algorithm’s ability and the superiority of the method developed for optimum product design processes.

The equilibrium optimization algorithm and the response surface-based metamodel for optimal structural design of vehicle components

2020 ◽  
Vol 62 (5) ◽  
pp. 492-496
Author(s):  
Ali Rıza Yıldız ◽  
H. Özkaya ◽  
M. Yıldız ◽  
S. Bureerat ◽  
B. S. Yıldız ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Automotive Industry ◽  
Structural Design ◽  
Optimization Problem ◽  
Low Cost ◽  
Design Problems ◽  
Structural Design Optimization ◽  
Vehicle Components ◽  
Vehicle Seat ◽  
Real World Problems

Abstract Due to harsh competitive conditions and the transition to new vehicles such as hybrid and full-electrical, the interest in the design of light and low-cost vehicles is increasing. In this paper, a recent metaheuristic procedure which is an equilibrium optimization algorithm (EOA) is used to solve a structural design optimization problem for a vehicle seat bracket to prove how the EOA can be used in solving industrial design problems. This paper is the first application of the EAO to real-world problems in the literature. The results strongly prove the capability of the EOA for designing optimum components in the automotive industry.

scholarly journals A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2556 ◽  
Author(s):  
Mohamed Tolba ◽  
Hegazy Rezk ◽  
Ahmed A. Zaki Diab ◽  
Mujahed Al-Dhaifallah
Keyword(s):  
Radial Distribution ◽  
Optimization Algorithm ◽  
Distribution Networks ◽  
Practical Case ◽  
Salp Swarm Algorithm ◽  
Distributed Generators ◽  
Hybrid Particle Swarm Optimization ◽  
Grasshopper Optimization Algorithm ◽  
Radial Distribution Networks ◽  
Swarm Algorithm

A novel methodology based on the recent metaheuristic optimization algorithm Salp Swarm Algorithm (SSA) for locating and optimal sizing of renewable distributed generators (RDGs) and shunt capacitor banks (SCBs) on radial distribution networks (RDNs) is proposed. A multi-objective function index (MOFI) approach is used for assuring the power quality (PQ) through enhancing the voltage level in addition to minimizing the power losses of the system and the whole operating cost of the grid. The proposed methodology is tested via 33-Bus standard radial distribution networks at different scenarios to prove their validity and performance. The obtained results are compared with the Grasshopper Optimization Algorithm (GOA), and the hybrid Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (PSOGSA). The SSA optimizer proved its superiority with high attitude and accuracy for solving the problems of RDGs’ and SCBs’ locations and capacities simultaneously. An Egyptian practical case study at different load levels via different scenarios including the control operation within 24 h is considered.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

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