Particle swarm optimization with mutation operations controlled by landscape modality estimation using hill-valley detection

Particle swarm optimization (PSO) is one of swarm intelligence algorithms and has been used to solve various optimization problems. Since the performance of PSO is much affected by the algorithm parameters of PSO, studies on adaptive control of the parameters have been done. Adaptive PSO (APSO) is o...

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Bibliographic Details
Published in:Artificial life and robotics 2016-12, Vol.21 (4), p.423-433
Main Authors: Takahama, Tetsuyuki, Sakai, Setsuko, Kushida, Jun-ichi, Hara, Akira
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Artificial Intelligence
Computation by Abstract Devices
Computer Science
Control
Convergence
Divergence
Graphs
Landscapes
Mechatronics
Mutation
Mutations
Parameter identification
Parameters
Particle swarm optimization
Robotics
Searching
Special Feature: Original Article
Swarm intelligence
Valleys
Citations: Items that this one cites
Online Access:Get full text
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Summary:Particle swarm optimization (PSO) is one of swarm intelligence algorithms and has been used to solve various optimization problems. Since the performance of PSO is much affected by the algorithm parameters of PSO, studies on adaptive control of the parameters have been done. Adaptive PSO (APSO) is one of representative studies. Parameters are controlled according to the evolutionary state, where the state is estimated by distance relations among a best search point and other search points. Also, a global Gaussian mutation operation is introduced to escape from local optima. In this study, a new adaptive control based on landscape modality estimation using hill-valley detection is proposed. A proximity graph is created from search points, hills and valleys are detected in the graph, landscape modality of an objective function is identified as unimodal or multimodal. Parameters are adaptively controlled as: parameters for convergence are selected in unimodal landscape and parameters for divergence are selected in multimodal landscape. Also, two mutation operations are introduced according to the modality. In unimodal landscape, a new local mutation operation is applied to the worst hill point which will be moved toward the best point for convergence. In multimodal landscape, a new adaptive global mutation operation is applied to all hill points for escaping from local optima. The advantage of the proposed method is shown by comparing the results of the method with those by PSO with fixed parameters and APSO.
ISSN:1433-5298
1614-7456
DOI:10.1007/s10015-016-0299-0