Abstract
Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems.
Highlights
Global optimization problems are common in engineering and other related fields [1,2,3], and it is usually difficult to solve the global optimization problems due to many local optima and complex search space, especially in high dimensions
90% sig. level Best rank (b) Standard deviation stdPSO, CPSO, FIPS, and Frankenstein exceed the lines of significant level, which indicates that the PS-FW performs significantly better than these four algorithms over the solutions accuracy
Friedman ranks of Algorithms PS-FW stdPso CPSO CLPSO FIPS Frankenstein AIWPSO
Summary
Global optimization problems are common in engineering and other related fields [1,2,3], and it is usually difficult to solve the global optimization problems due to many local optima and complex search space, especially in high dimensions. Many effective metaheuristic algorithms have been presented, such as simulated annealing (SA) [4], differential evolution (DE) [5], genetic algorithm (GA) [6], particle swarm optimization (PSO) [7], ant colony optimization (ACO) [8], artificial bee colony (ABC) [9], and fireworks algorithm (FWA) [10]. Among these intelligent algorithms, the PSO and FWA have shown pretty outstanding performance in solving global optimization problems in the last several years. Owing to the less decision parameters, simple implementation, and good scalability, PSO and FWA have been widely applied since they were proposed, including shunting schedule optimization of electric multiple units depot [11], optimal operation of trunk natural gas pipelines [12], location optimization of logistics distribution center [13], artificial neural networks design [14], warehousescheduling [15], fertilization optimization [16], power system reconfiguration [17], and multimodal function optimization [18]
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