A Culture-Based Particle Swarm Optimization Framework for Dynamic, Constrained Multi-Objective Optimization

Real-world optimization problems are often dynamic, multiple objective in nature with various constraints and uncertainties. This work proposes solving such problems by systematic segmentation via heuristic information accumulated through Cultural Algorithms. The problem is tackled by maintaining 1)...

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Bibliographic Details
Published in:International journal of swarm intelligence research Vol. 3; no. 1; pp. 1 - 29
Main Authors: Yen, Gary G, Kadkol, Ashwin A
Format: Journal Article
Language:English
Published: Hershey IGI Global 01.01.2012
Subjects:
Algorithms
Constraints
Dynamics
Mathematical models
Multiple objective analysis
Optimization
Parameters
Particle swarm optimization
Proportional integral derivative
Relocation
Robustness (mathematics)
Searching
Segmentation
Transfer functions
Tuning
ISSN:1947-9263, 1947-9271
Online Access:Get full text
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Summary:Real-world optimization problems are often dynamic, multiple objective in nature with various constraints and uncertainties. This work proposes solving such problems by systematic segmentation via heuristic information accumulated through Cultural Algorithms. The problem is tackled by maintaining 1) feasible and infeasible best solutions and their fitness and constraint violations in the Situational Space, 2) objective space bounds for the search in the Normative Space, 3) objective space crowding information in the Topographic Space, and 4) function sensitivity and relocation offsets (to reuse available information on optima upon change of environments) in the Historical Space of a cultural framework. The information is used to vary the flight parameters of the Particle Swarm Optimization, to generate newer individuals and to better track dynamic and multiple optima with constraints. The proposed algorithm is validated on three numerical optimization problems. As a practical application case study that is computationally intensive and complex, parameter tuning of a PID (Proportional–Integral–Derivative) controller for plants with transfer functions that vary with time and imposed with robust optimization criteria has been used to demonstrate the effectiveness and efficiency of the proposed design.
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ISSN:1947-9263
1947-9271
DOI:10.4018/jsir.2012010101