A Hybrid Glowworm Swarm Optimization Algorithm for Constrained Engineering Design Problems

2013 ◽  
Vol 7 (1) ◽  
pp. 379-388 ◽  
Author(s):  
Yongquan Zhou ◽  
Guo Zhou ◽  
Junli Zhang
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Design Problems ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Engineering Design Problems

scholarly journals Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

2020 ◽  
Vol 13 (6) ◽  
pp. 279-293
Author(s):  
Hanan Akkar ◽  
◽  
Sameem Salman ◽  
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Optimization Methods ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Grasshopper Optimization Algorithm ◽  
Minimum Number ◽  
Grasshopper Optimization ◽  
Design Pressure

This paper proposes a new meta-heuristic swarm optimization algorithm called Cicada Swarm Optimization (CISO) algorithm, which mimics the behavior of bio-inspired swarm optimization methods. The CISO algorithm is tested with 23 benchmark functions and taken two problems engineering design, pressure vessel problem and himmelblau’s problem. The performance of CISO algorithm is compared with meta-heuristic well-known and recently proposed algorithms (Cockroach Swarm Optimization (CSO), Grasshopper Optimization algorithm (GOA) and Particle Swarm Optimization (PSO)). The obtained results showed that the proposed algorithm succeeded in improving the test functions and solved engineering design problems that could not be improved by other algorithms according to the chosen parameters and the limits of the research space, also showed that CISO has a faster convergence with the minimum number of iterations and also have an accurate calculation efficiency Satisfactory compared to other optimization algorithms.

Nonlinear Mixed Integer-Discrete-Continuous Programming and its Application to Engineering Design Problems

1989 ◽  
Author(s):  
J.-F. Fu ◽  
R. G. Fenton ◽  
W. L. Cleghorn
Keyword(s):  
Nonlinear Programming ◽  
Objective Function ◽  
Engineering Design ◽  
Optimization Algorithm ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Design Problems ◽  
Continuous Programming ◽  
Engineering Design Problems ◽  
Standard Values

Abstract An algorithm for solving nonlinear programming problems containing integer, discrete and continuous variables is presented. Based on a commonly employed optimization algorithm, penalties on integer and/or discrete violations are imposed on the objective function to force the search to converge onto standard values. Examples are included to illustrate the practical use of this algorithm.

An Effective Chaotic Cultural-Based Particle Swarm Optimization for Constrained Engineering Design Problems

2010 ◽  
Vol 20-23 ◽  
pp. 64-69 ◽  
Author(s):  
Yong Quan Zhou ◽  
Lingzi Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Engineering Design ◽  
Search Strategy ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Local Search Strategy

In this paper, a novel chaotic cultural-based particle swarm optimization algorithm (CCPSO) is proposed for constrained optimization problems by employing cultural-based particle swarm optimization (CPSO) algorithm and the notion of chaotic local search strategy. In the CCPSO, the shortcoming of cultural-based particle swarm optimization (CPSO) that it is easy to trap into local minimum be overcome, the chaotic local search strategy is introduced in the influence functions of cultural algorithm. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed method.

scholarly journals Beetle swarm optimization algorithm: Theory and application

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5121-5137
Author(s):  
Tiantian Wang ◽  
Long Yang ◽  
Qiang Liu
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Pso Algorithm ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Grasshopper Optimization Algorithm ◽  
Algorithm Theory ◽  
Grasshopper Optimization ◽  
Practical Impact

In this paper, a new meta-heuristic algorithm, called beetle swarm optimization (BSO) algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and grasshopper optimization algorithm (GOA). Numerical experiments show that the BSO algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblau?s optimization problem, are also considered and the proposed BSO algorithm is shown to be competitive in those applications.

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