Abstract
Bacterial colony chemotaxis algorithm was originally developed for optimal problem with continuous space. In this paper the discrete bacterial colony chemotaxis (DBCC) algorithm is developed to solve multiobjective optimization problems. The basic DBCC algorithm has the disadvantage of being trapped into the local minimum. Therefore, some improvements are adopted in the new algorithm, such as adding chaos transfer mechanism when the bacterium choose their next locations and the crowding distance operation to maintain the population diversity in the Pareto Front. The definition of chaos transfer mechanism is used to retain the elite solution produced during the operation, and the definition of crowding distance is used to guide the bacteria for determinate variation, thus enabling the algorithm obtain well-distributed solution in the Pareto optimal set. The convergence properties of the DBCC strategy are tested on some test functions. At last, some numerical results are given to demonstrate the effectiveness of the results obtained by the new algorithm.
Highlights
In the field of optimization, many researchers have been inspired by biological processes such as evolution [1, 2] or the food-searching behavior of ants [3] to develop new optimal methods such as evolutionary algorithms or ant codes
A multiobjective optimal problems (MOP) [15] is defined as the problem of finding a vector of decision variables that satisfies some restrictions and optimizes a vector function, whose elements represent the values of the functions
From the table of comparisons, the results show that all indexes of Multiobjective Optimal Algorithm Based on DBCC (MOADBCC) with operators are better than that of MOADBCC without operators
Summary
In the field of optimization, many researchers have been inspired by biological processes such as evolution [1, 2] or the food-searching behavior of ants [3] to develop new optimal methods such as evolutionary algorithms or ant codes These techniques have been found to perform better than the classical heuristic or gradient-based optimization methods, especially when faced with the problem of optimizing multimodal, nondifferentiable, or discontinuous functions. Examples of applying these biologically inspired strategies in the field of engineering range from aerodynamic design [4] to job-shop scheduling problems [5].
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