Abstract
Many cooperative coevolution optimization algorithms have been proposed recently for solving large-scale global optimization problems. These algorithms first decompose a large-scale global optimization problem into several subproblems, each with a specific number of decision variables, and then optimize the subproblems separately. However, if computing resources are not reasonably allocated to subproblems, computational resources may be wasted. In this paper, we propose a distributed contribution-based quantum-behaved particle swarm optimization with controlled diversity (DC-QPSO) for large-scale global optimization problems. According to the level of optimized contribution of each subproblem, the computing resources are reallocated automatically in each stage, guaranteeing that subgroups with more contribution get more computational resources. Moreover, a parallel diversity control strategy is proposed to enhance the capability of finding better solutions to problems. CEC'2010 and CEC'2013 benchmark function suits are selected to test the performance of the proposed algorithms. The experimental results demonstrate the better performance of the proposed DC-QPSO on the benchmark functions, compared to other state-of-the-art large-scale global optimization algorithms.
Highlights
Particle swarm optimization [1] (PSO) algorithm is one of the important population-based random search algorithms for optimization problems
The main reason is that the proposed algorithm is designed for separable functions, and we selected those separable functions as the same as in DLLSO
The advantages are: First, We found that lower convergence is the essential problem when apply quantum-behaved particle swarm optimization (QPSO) into large-scale global optimization problem (LSGO) problem, and the contribution based computational budget allocation method was proposed to address this problem
Summary
Particle swarm optimization [1] (PSO) algorithm is one of the important population-based random search algorithms for optimization problems. It is has been widely applied in various areas such as transportation [27], military industry [28], medical industry [29] and other real-world problems [45]– [47]. Almost all versions of PSO, together with other populationbased methods (i.e. evolutionary algorithms), may lose their efficiency when solving a large-scale global optimization problem (LSGO) because of the following reasons. The high-dimensional problem have a wider search space, which makes it difficult to find the neighborhood region of global optimal solution.
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