Convergence Analysis of Adaptive Tabu Search
نویسندگان
چکیده
This paper presents a convergence proof of the adaptive tabu search (ATS) algorithms. The proof consists of two parts, i.e. convergence proof of all interested solutions in a finite search space, and that of searching processes of the ATS algorithms to the global minimum. With the proposed definitions and theorems, the proofs show that the ATS algorithms based on a random process have finite convergence. The searching process also converges to the (near) global minimum rapidly. Two applications, the global minimum finding of Bohachevsky’s function and the identification of the nonlinear pendulum model, serve to illustrate the effectiveness of the ATS algorithms.
منابع مشابه
Finite Convergence and Performance Evaluation of Adaptive Tabu Search
The naïve tabu search (NTS) has been enhanced with two adaptive mechanisms namely back-tracking and adaptive search radius. The proposed search is called adaptive tabu search (ATS). The paper provides convergence and performance analyses of the ATS.
متن کاملA novel heuristic algorithm for capacitated vehicle routing problem
The vehicle routing problem with the capacity constraints was considered in this paper. It is quite difficult to achieve an optimal solution with traditional optimization methods by reason of the high computational complexity for large-scale problems. Consequently, new heuristic or metaheuristic approaches have been developed to solve this problem. In this paper, we constructed a new heuristic ...
متن کاملAction Inhibition
An explicit exploration strategy is necessary in reinforcement learning (RL) to balance the need to reduce the uncertainty associated with the expected outcome of an action and the need to converge to a solution. This dependency is more acute in on-policy reinforcement learning where the exploration guides the search for an optimal solution. The need for a self-regulating exploration is manifes...
متن کاملApplication of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting
Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the se...
متن کاملApplication of Adaptive Tabu Search Algorithm in Sinusoidal Fryze Voltage Control based Hybrid Series Active Power Filter
A novel hybrid series active power filter to eliminate harmonics and compensate reactive power is presented and analyzed. The proposed active compensation technique is based in a hybrid series active filter using adaptive Tabu search (ATS) algorithm in the conventional Sinusoidal Fryze voltage (SFV) control technique. Optimization of the conventional Sinusoidal Fryze voltage control technique h...
متن کامل