A Genetic Algorithm for Discrete Optimization of Space Trusses with Plastic Collapse Constraints

2009 ◽  
Author(s):  
A. Csébfalvi
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
Plastic Collapse ◽  
Space Trusses

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

2021 ◽  
pp. 2150020
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Traveling Salesman ◽  
The Other ◽  
Discrete Optimization Problem ◽  
Social Spider ◽  
Social Spider Algorithm

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

A Genetic Algorithm with the Mixed Heuristics for Traveling Salesman Problem

2015 ◽  
Vol 14 (01) ◽  
pp. 1550003 ◽  
Author(s):  
Yong Wang
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Discrete Optimization ◽  
Time Complexity ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Optimization Process ◽  
Hamiltonian Cycles ◽  
Improved Genetic Algorithm ◽  
Discrete Optimization Problems

Traveling salesman problem (TSP) is one of well-known discrete optimization problems. The genetic algorithm is improved with the mixed heuristics to resolve TSP. The first heuristics is the four vertices and three lines inequality, which is applied to the 4-vertex paths to generate the shorter Hamiltonian cycles (HC). The second local heuristics is executed to reverse the i-vertex paths with more than two vertices, which also generates the shorter HCs. It is necessary that the two heuristics coordinate with each other in the optimization process. The time complexity of the first and second heuristics are O(n) and O(n3), respectively. The two heuristics are merged into the original genetic algorithm. The computation results show that the improved genetic algorithm with the mixed heuristics can find better solutions than the original GA does under the same conditions.

Investigating the Statical Stability of Pin-jointed Structures Using Genetic Algorithm

2005 ◽  
Vol 20 (1) ◽  
pp. 53-68 ◽  
Author(s):  
Sana El-Lishani ◽  
H. Nooshin ◽  
P. Disney
Keyword(s):  
Genetic Algorithm ◽  
Quadratic Form ◽  
Computer Program ◽  
Search Technique ◽  
Cable Structures ◽  
The Stability ◽  
Indeterminate Structures ◽  
Cable Nets ◽  
2D And 3D ◽  
Space Trusses

In this paper, the method of genetic algorithm is used as a search technique to find the stability characteristics of simultaneously statically and kinematically indeterminate structures The genetic algorithm is used to find out if there is a solution for a specific quadratic form which has to be satisfied in order to guarantee the statical stability. The genetic algorithm is a search technique that imitates nature in selecting and optimising towards an aim. The use of the genetic algorithm in the search for the stability of pin-jointed structures is found to be simple and powerful. A computer program called STAPS (Stability of Pin-jointed Structures) has been developed using the genetic algorithm. This program firstly identifies the independent mechanisms and states of self-stress, if any, in a structure. Then, the program searches for any state of self-stress that can stabilise all the mechanisms of the structure. STAPS program is a powerful tool for finding the stability of 2D and 3D pin-jointed structures. The program can be used for investigating the stability of space trusses and cable structures like cable nets, cable-strut and tensegrity structures1,2. Section 1 of this paper contains a brief introduction. Section 2 discusses the background of what is called ‘product forces’. Also, in Section 3 the stabilisation of mechanisms of pin-jointed structures is discussed. Section 4 introduces the method of genetic algorithm and how it is used in the search for stability of pin-jointed structures. Section 5 introduces the STAPS program together with illustrative examples of its application. Finally, Section 6 gives a conclusion of the work presented in this paper.

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