An oracle penalty and modified augmented Lagrangian methods with firefly algorithm for constrained optimization problems

Almost all engineering optimization problems in the real world are constrained in nature. Swarm intelligence is a bio-inspired technique based on studying and observing fireflies, ants, birds and fish in nature. Firefly algorithm (FA) is the most prominent swarm based metaheuristic algorithm used fo...

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Bibliographic Details
Published in:Operational research 2020-06, Vol.20 (2), p.985-1010
Main Authors: Balande, Umesh, Shrimankar, Deepti
Format: Article
Language:English
Subjects:
Algorithms
Business and Management
Computational Intelligence
Constraints
Global optimization
Heuristic methods
Management Science
Operations Research
Operations Research/Decision Theory
Optimization algorithms
Original Paper
Statistical analysis
Swarm intelligence
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Summary:Almost all engineering optimization problems in the real world are constrained in nature. Swarm intelligence is a bio-inspired technique based on studying and observing fireflies, ants, birds and fish in nature. Firefly algorithm (FA) is the most prominent swarm based metaheuristic algorithm used for solving a global optimization problem. This paper presents two new constrained optimization algorithms: (1) firefly algorithm with extended oracle penalty method (FA-EOPM) and (2) modified augmented Lagrangian with firefly algorithm (MAL-FA). These proposed algorithms are applied for solving classic thirteen benchmark constraint problems as well as a few good engineering problem designs. The efficiency, effectiveness, and performance of MAL-FA and FA-EOPM algorithms are estimated on the basis of statistical analysis such as best optimal value, worst value, mean value, p value and standard deviation value against the existing methods. The experimental results show that the proposed MAL-FA algorithm offers better outcomes for most of the cases in terms of the number of function evaluations compared to various optimization algorithms.
ISSN:1109-2858
1866-1505
DOI:10.1007/s12351-017-0346-1