scholarly journals Killer Whale Algorithm: An Algorithm Inspired by the Life of Killer Whale

2017 ◽  
Author(s):  
Totok R. Biyanto
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Killer Whale ◽  
Maximum Velocity ◽  
Imperialist Competitive Algorithm ◽  
Global Optimum ◽  
Black Box ◽  
Mathematical Function ◽  
Potential Prey ◽  
Optimum Direction

This paper proposed a new algorithm inspired by the life of Killer Whale. A group of Killer Whale called Matriline that consist of a leader and members. The leader’s duty searches prey position and the optimum direction to chase the prey, meanwhile chase the prey is performed by the members. Optimum direction means minimum direction and maximum velocity. Global optimum is obtained by comparing the results of member’s actions. In this algorithm, if value of objective function of members more than leader, hence the leader must find out another new potential prey. In order to obtain the performance of proposed algorithm, it is necessary to test the new algorithm together with other algorithm using known mathematical function that available in Comparing Continuous Optimizers (COCO) especially Black Box Optimization Benchmarking (BBOB). Optimization results show that the performances of purposed algorithm has outperformed than others algorithms such as Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Simulated Annealing (SA).

scholarly journals Multidiscilinary design optimization for aircraft wing using response surface method, genetic algorithm, and simulated annealing

2020 ◽  
Vol 14 (1) ◽  
pp. 28-41
Author(s):  
Xuan-Binh Lam
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Response Surface ◽  
Response Surface Method ◽  
Multidisciplinary Design Optimization ◽  
Global Optimum ◽  
Multidisciplinary Design ◽  
Aircraft Wing ◽  
Surface Method

Multidisciplinary Design Optimization (MDO) has received a considerable attention in aerospace industry. The article develops a novel framework for Multidisciplinary Design Optimization of aircraft wing. Practically, the study implements a high-fidelity fluid/structure analyses and accurate optimization codes to obtain the wing with best performance. The Computational Fluid Dynamics (CFD) grid is automatically generated using Gridgen (Pointwise) and Catia. The fluid flow analysis is carried out with Ansys Fluent. The Computational Structural Mechanics (CSM) mesh is automatically created by Patran Command Language. The structural analysis is done by Nastran. Aerodynamic pressure is transferred to finite element analysis model using Volume Spline Interpolation. In terms of optimization algorithms, Response Surface Method, Genetic Algorithm, and Simulated Annealing are utilized to get global optimum. The optimization objective functions are minimizing weight and maximizing lift/drag. The design variables are aspect ratio, tapper ratio, sweepback angle. The optimization results demonstrate successful and desiable construction of MDO framework. Keywords: Multidisciplinary Design Optimization; fluid/structure analyses; global optimum; Genetic Algorithm; Response Surface Method.

Development of Global Optimization Method for Design of Turbine Stages

2005 ◽  
Author(s):  
Hiroyuki Kawagishi ◽  
Kazuhiko Kudo
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Simulated Annealing ◽  
Optimum Design ◽  
Optimization Method ◽  
Global Optimum ◽  
Global Optimization Method ◽  
Optimum Solution ◽  
Global Optimum Solution ◽  
Number Of Iterations

A new optimization method which can search for the global optimum solution and decrease the number of iterations was developed. The performance of the new method was found to be effective in finding the optimum solution for single- and multi-peaked functions for which the global optimum solution was known in advance. According to the application of the method to the optimum design of turbine stages, it was shown that the method can search the global optimum solution at approximately one seventh of the iterations of GA (Genetic Algorithm) or SA (Simulated Annealing).

Parallel recombinative simulated annealing: A genetic algorithm

1995 ◽  
Vol 21 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Samir W. Mahfoud ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing

Imperialist Competitive Algorithm for Multiobjective Optimization of Ellipsoidal Head of Pressure Vessel

2011 ◽  
Vol 110-116 ◽  
pp. 3422-3428 ◽  
Author(s):  
Behzad Abdi ◽  
Hamid Mozafari ◽  
Ayob Amran ◽  
Roya Kohandel
Keyword(s):  
Genetic Algorithm ◽  
Pressure Vessel ◽  
Shear Force ◽  
Minimum Weight ◽  
Bending Moment ◽  
Imperialist Competitive Algorithm ◽  
Optimum Shape ◽  
Working Pressure ◽  
Competitive Algorithm ◽  
Bending Stresses

This work devoted to an ellipsoidal head of pressure vessel under internal pressure load. The analysis is aimed at finding an optimum weight of ellipsoidal head of pressure vessel due to maximum working pressure that ensures its full charge with stresses by using imperialist competitive algorithm and genetic algorithm. In head of pressure vessel the region of its joint with the cylindrical shell is loaded with shear force and bending moments. The load causes high bending stresses in the region of the joint. Therefore, imperialist competitive algorithm was used here to find the optimum shape of a head with minimum weight and maximum working pressure which the shear force and the bending moment moved toward zero. Two different size ellipsoidal head examples are selected and studied. The imperialist competitive algorithm results are compared with the genetic algorithm results.

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

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