A Self-Adaptive Heuristic Algorithm for Combinatorial Optimization Problems
نویسندگان
چکیده
منابع مشابه
A Self-Adaptive Heuristic Algorithm for Combinatorial Optimization Problems
This paper introduces a new self-tuning mechanism to the local search heuristic for solving of combinatorial optimization problems. Parameter tuning of heuristics makes them difficult to apply, as parameter tuning itself is an optimization problem. For this purpose, a modified local search algorithm free from parameter tuning, called SelfAdaptive Local Search (SALS), is proposed for obtaining q...
متن کاملEvolution Hyper - heuristic for Combinatorial Optimization problems
Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g. ...
متن کاملAdaptive Quantum Inspired Genetic Algorithm for Combinatorial Optimization Problems
The development in the field of quantum computing gives us a significant edge over classical computing in terms of time and efficiency. This is particularly useful for NP-hard problems such as graph layout problems. Since many real world problems are effectively solved by genetic algorithm (GA) and the performance of GA highly depends upon the setting of its parameters, therefore this paper foc...
متن کاملA New Optimization Algorithm For Combinatorial Problems
Combinatorial optimization problems are those problems that have a finite set of possible solutions. The best way to solve a combinatorial optimization problem is to check all the feasible solutions in the search space. However, checking all the feasible solutions is not always possible, especially when the search space is large. Thus, many meta-heuristic algorithms have been devised and modifi...
متن کاملSelf-Adaptive Spider Monkey Optimization Algorithm for Engineering Optimization Problems
Algorithms inspired by intelligent behavior of simple agents are very popular now a day among researchers. A comparatively young algorithm motivated by extraordinary behavior of Spider Monkeys is Spider Monkey Optimization (SMO) algorithm. SMO algorithm is very successful algorithm to get to the bottom of optimization problems. This work presents a self-adaptive Spider Monkey optimization (SaSM...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computational Intelligence Systems
سال: 2014
ISSN: 1875-6891,1875-6883
DOI: 10.1080/18756891.2014.966992