Stochastic Local Search Using the Search Space Smoothing Meta-Heuristic: A Case Study
نویسندگان
چکیده
In this paper, two smoothing effects are firstly pointed out by analysis and by experiment on Traveling Salesman Problem(TSP) instances. We design a novel algorithm which runs stochastic local search under the SSS framework. The function determining the accepting probability of uphill moves is designed so that the algorithm can take advantage of the local smoothing effect ignored in original SSS. Experimental results on TSPLIB instances demonstrated that the performance of the new algorithm is much superior to traditional SSS approach.
منابع مشابه
Training Radial Basis Function Neural Network using Stochastic Fractal Search Algorithm to Classify Sonar Dataset
Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...
متن کاملA Discrete Hybrid Teaching-Learning-Based Optimization algorithm for optimization of space trusses
In this study, to enhance the optimization process, especially in the structural engineering field two well-known algorithms are merged together in order to achieve an improved hybrid algorithm. These two algorithms are Teaching-Learning Based Optimization (TLBO) and Harmony Search (HS) which have been used by most researchers in varied fields of science. The hybridized algorithm is called A Di...
متن کاملCONFIGURATION OPTIMIZATION OF TRUSSES USING A MULTI HEURISTIC BASED SEARCH METHOD
Different methods are available for simultaneous optimization of cross-section, topology and geometry of truss structures. Since the search space for this problem is very large, the probability of falling in local optimum is considerably high. On the other hand, different types of design variables (continuous and discrete) lead to some difficulties in the process of optimization. In this articl...
متن کاملUsing Greedy Randomize Adaptive Search Procedure for solve the Quadratic Assignment Problem
Greedy randomize adaptive search procedure is one of the repetitive meta-heuristic to solve combinatorial problem. In this procedure, each repetition includes two, construction and local search phase. A high quality feasible primitive answer is made in construction phase and is improved in the second phase with local search. The best answer result of iterations, declare as output. In this stu...
متن کاملGravitational Search Algorithm to Solve the K-of-N Lifetime Problem in Two-Tiered WSNs
Wireless Sensor Networks (WSNs) are networks of autonomous nodes used for monitoring an environment. In designing WSNs, one of the main issues is limited energy source for each sensor node. Hence, offering ways to optimize energy consumption in WSNs which eventually increases the network lifetime is strongly felt. Gravitational Search Algorithm (GSA) is a novel stochastic population-based meta-...
متن کامل