Escaping Local Optima via Parallelization and Migration
نویسندگان
چکیده
We present a new nature-inspired algorithm, mt − GA, which is a parallelized version of a simple GA, where subpopulations evolve independently from each other and on different threads. The overall goal is to develop a population-based algorithm capable to escape from local optima. In doing so, we used complex trap functions, and we provide experimental answers to some crucial implementation decision problems. The obtained results show the robustness and efficiency of the proposed algorithm, even when compared to well-known state-of-the art optimization algorithms based on the clonal selection principle.
منابع مشابه
Parallel Variable Neighborhood Searches
Variable Neighborhood Search (VNS) is a recent and effective metaheuristic for solving combinatorial and global optimization problems. It is capable of escaping from the local optima by systematic changes of the neighborhood structures within the search. In this paper several parallelization strategies for VNS have been proposed and compared on the large instances of the p-median problem. ∗The ...
متن کاملDynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search
Local search algorithms often get trapped in local optima. Algorithms such as tabu search and simulated annealing ’escape’ local optima by accepting nonimproving moves. Another possibility is to dynamically change between representations; a local optimum under one representation may not be a local optimum under another. Shifting is a mechanism which dynamically switches between Gray code repres...
متن کاملThe Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration
Abstract. We use the Local Optima Network model to study the structure of symmetric TSP fitness landscapes. The ‘big-valley’ hypothesis holds that for TSP and other combinatorial problems, local optima are not randomly distributed, instead they tend to be clustered around the global optimum. However, a recent study has observed that, for solutions close in evaluation to the global optimum, this...
متن کاملEscaping Local Optima: Constraint Weights vs. Value Penalties
Constraint Satisfaction Problems can be solved using either iterative improvement or constructive search approaches. Iterative improvement techniques converge quicker than the constructive search techniques on large problems, but they have a propensity to converge to local optima. Therefore, a key research topic on iterative improvement search is the development of effective techniques for esca...
متن کاملTabu-KM: A Hybrid Clustering Algorithm Based on Tabu Search Approach
The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013