scholarly journals Simulated Annealing as an Intensification Component in Hybrid Population-based Metaheuristics

10.5772/5570 ◽  
2008 ◽  
Author(s):  
Davide Anghinolfi ◽  
Massimo Paolucci
Keyword(s):  
Simulated Annealing ◽  
Population Based ◽  
Hybrid Population

Solving the Laser Cutting Path Problem Using Population-Based Simulated Annealing with Adaptive Large Neighborhood Search

2020 ◽  
Vol 833 ◽  
pp. 29-34 ◽  
Author(s):  
Makbul Hajad ◽  
Viboon Tangwarodomnukun ◽  
Chaiya Dumkum ◽  
Chorkaew Jaturanonda
Keyword(s):  
Simulated Annealing ◽  
Laser Cutting ◽  
Computing Time ◽  
Population Based ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Adaptive Large Neighborhood Search ◽  
Large Neighborhood ◽  
Generalized Traveling Salesman Problem ◽  
Cutting Path

This paper presents an alternative algorithm for solving the laser cutting path problem which was modeled as Generalized Traveling Salesman Problem (GTSP). The objective is to minimize the traveling distance of laser cutting of all profiles in a given layout, where a laser beam makes a single visit and then does the complete cut of individual profile in an optimum sequence. This study proposed a hybrid method combining population-based simulated annealing (SA) with an adaptive large neighborhood search (ALNS) algorithm to solve the cutting path problem. Recombination procedures were executed alternately using swap, reversion, insertion and removal-insertion through a fitness proportionate selection mechanism. In order to reduce the computing time and maintain the solution quality, the 35% proportion of population were executed in each iteration using the cultural algorithm selection method. The results revealed that the algorithm can solve several ranges of problem size with an acceptable percentage of error compared to the best known solution.

Hybrid Evolution Strategies Algorithm Based on Dynamic Demes

2012 ◽  
Vol 424-425 ◽  
pp. 174-178
Author(s):  
Ming Zhang ◽  
Jiang Hua Sui
Keyword(s):  
Simulated Annealing ◽  
Global Convergence ◽  
Maximum Entropy Principle ◽  
Population Based ◽  
Global Optimum ◽  
Evolution Strategies ◽  
Evolutionary Strategy ◽  
Entropy Principle ◽  
The Individual ◽  
Global Convergence Rate

A hybrid evolution strategies algorithm based on dynamic demes is proposed in this paper. Evolutionary strategy algorithm is likely premature, and even simulated annealing with the character of local search is impossible to escape this range, when change the population based on the maximum entropy principle so that the individual out of this range, which can quickly converge to the global optimum. The numerical computation results indicate that the algorithm can gain higher global convergence rate and higher speed

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