Abstract
This paper proposes a novel Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for solving high-dimensional problems. BBPSO is a variant of Particle Swarm Optimization (PSO) and is based on a Gaussian distribution. The BBPSO algorithm does not consider the selection of controllable parameters for PSO and is a simple but powerful optimization method. This algorithm, however, is vulnerable to high-dimensional problems, i.e., it easily becomes stuck at local optima and is subject to the “two steps forward, one step backward” phenomenon. This study improves its performance for high-dimensional problems by combining heterogeneous cooperation based on the exchange of information between particles to overcome the “two steps forward, one step backward” phenomenon and a jumping strategy to avoid local optima. The CEC 2010 Special Session on Large-Scale Global Optimization (LSGO) identified 20 benchmark problems that provide convenience and flexibility for comparing various optimization algorithms specifically designed for LSGO. Simulations are performed using these benchmark problems to verify the performance of the proposed optimizer by comparing the results of other variants of the PSO algorithm.
Highlights
Many optimization problems in modern engineering, e.g., optimal design and scheduling problems, must be solved with finite resources that should be used efficiently
The scoring system used in the CEC 2010 Large-Scale Global Optimization (LSGO) Challenge was as follows: for each algorithm, a table of the type shown in Table 7 that contains 300 competition categories was formed
The proposed HCBBPSO-Jx algorithms performed better than the other algorithms; they won for a total of 12 functions in terms of the mean Number of Function Evaluations (NFEs), which is similar to the results obtained using the CEC 2010 LSGO Challenge scoring system
Summary
Many optimization problems in modern engineering, e.g., optimal design and scheduling problems, must be solved with finite resources that should be used efficiently. There have been many studies on PSO variants that use Gaussian distributions [16,17,18], which can reduce or remove some of the parameters Of these algorithms, the Bare-Bones PSO (BBPSO) algorithm [19] is the simplest, and it can be intuitively understood and implemented without considering the parameter settings by sampling new particle positions from a Gaussian distribution whose mean is given by the average of the globally and locally best positions and whose standard deviation is given by the distance between the globally and locally best positions. This paper attempts to solve these problems and proposes an effective BBPSO-based optimizer for high-dimensional problems that combines heterogeneous cooperation based on the exchange of information between particles and a jumping strategy to avoid local optima.
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