Genetic Algorithm for Solving the Problem of an Optimum Regression Model Construction as a Discrete Optimization Problem

2008 ◽  
Vol 40 (6) ◽  
pp. 60-71 ◽  
Author(s):  
Ivan M. Melnik
Keyword(s):  
Genetic Algorithm ◽  
Regression Model ◽  
Discrete Optimization ◽  
Optimization Problem ◽  
Model Construction ◽  
Discrete Optimization Problem

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.

Optimal Viscous Damper Placement Using Level Set Topology Optimization Method

2010 ◽  
Author(s):  
Masoud Ansari ◽  
Amir Khajepour ◽  
Ebrahim Esmailzadeh
Keyword(s):  
Topology Optimization ◽  
Level Set ◽  
Discrete Optimization ◽  
Optimization Problem ◽  
Minimum Energy ◽  
Optimization Method ◽  
Discrete Optimization Problem ◽  
Placement Problem ◽  
New Approach ◽  
Optimal Damping

Vibration control has always been of great interest for many researchers in different fields, especially mechanical and civil engineering. One of the key elements in control of vibration is damper. One way of optimally suppressing unwanted vibrations is to find the best locations of the dampers in the structure, such that the highest dampening effect is achieved. This paper proposes a new approach that turns the conventional discrete optimization problem of optimal damper placement to a continuous topology optimization. In fact, instead of considering a few dampers and run the discrete optimization problem to find their best locations, the whole structure is considered to be connected to infinite numbers of dampers and level set topology optimization will be performed to determine the optimal damping set, while certain number of dampers are used, and the minimum energy for the system is achieved. This method has a few major advantages over the conventional methods, and can handle damper placement problem for complicated structures (systems) more accurately. The results, obtained in this research are very promising and show the capability of this method in finding the best damper location is structures.

scholarly journals Are Stable Instances Easy?

2012 ◽  
Vol 21 (5) ◽  
pp. 643-660 ◽  
Author(s):  
YONATAN BILU ◽  
NATHAN LINIAL
Keyword(s):  
Polynomial Time ◽  
Discrete Optimization ◽  
Optimization Problem ◽  
Discrete Optimization Problem ◽  
Hard Problem ◽  
Np Hard ◽  
Max Cut Problem ◽  
Np Hard Problem ◽  
Hard Problems ◽  
Np Hard Problems

We introduce the notion of a stable instance for a discrete optimization problem, and argue that in many practical situations only sufficiently stable instances are of interest. The question then arises whether stable instances of NP-hard problems are easier to solve, and in particular, whether there exist algorithms that solve in polynomial time all sufficiently stable instances of some NP-hard problem. The paper focuses on the Max-Cut problem, for which we show that this is indeed the case.

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