A Staged Continuous Tabu Search Algorithm for the Global Optimization and its Applications to the Design of Fiber Bragg Gratings

Abstract

A novel staged continuous Tabu search (SCTS) algorithm is proposed for solving global optimization problems of multi-minima functions with multi-variables. The proposed method comprises three stages that are based on the continuous Tabu search (CTS) algorithm with different neighbor-search strategies, with each devoting to one task. The method searches for the global optimum thoroughly and efficiently over the space of solutions compared to a single process of CTS. The effectiveness of the proposed SCTS algorithm is evaluated using a set of benchmark multimodal functions whose global and local minima are known. The numerical test results obtained indicate that the proposed method is more efficient than an improved genetic algorithm published previously. The method is also applied to the optimization of fiber grating design for optical communication systems. Compared with two other well-known algorithms, namely, genetic algorithm (GA) and simulated annealing (SA), the proposed method performs better in the optimization of the fiber grating design.

A Modified Staged Continuous Tabu Search Algorithm

“…[9] were taken as test functions. The results of MSCTS algorithm were compared with those of SCTS algorithm.…”

“…The parameters of SCTS algorithm are set according to Ref. [9]. In the computational experiment, the performance of optimization algorithm is evaluated by 5 performance indexes which are described in …”

“…Many method were proposed to solve this kind shortage, such as using a specific neighborhood definition which employs a block of jobs notion [6], employing a flexible memory system to avoid the entrapment in a local minimum and developing the ideal of "distance" to the fitness to accelerate optimization [7], processing a new parallel model for TS algorithm based on the crossover operator of genetic algorithms [8], and using three continuous stages in process like stage continuous tabu search (SCTS) algorithm [9]. Each stage in SCTS algorithm will produce a starting point for next stage.…”

Hybrid optimization with improved tabu search

“…The only information which is obtainable in these unknown systems is the input-output behaviour. As examples in engineering applications; one can point at fluid power circuits [10], electronic circuit designs [2], balancing of hydraulic turbines [43], computational chemistry [40], developing optical fibers [51], signal setting problems [6], non-destructive control [5], optimization of electromagnetic devices [21] and others. The diversity of applications has led to a great deal of interest to develop global continuous optimization methods (GCOM) which are capaDue to the lack of information about the behaviour of an unknown system, no specific assumptions are being made.…”

“…In the first phase, the most promising areas are defined and then in the second phase the promising areas are intensified. In another recent work a three-phase TS approach has been proposed [51]. In the first phase of this approach the promising areas are recognized.…”