Multi-Objective Flower Algorithm for Optimization

By Xin-She Yang, Mehmet Karamanoglu, Xingshi He. International Conference on Conceptual Structures, 2014

384 citations recorded by the source corpus.

Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective flower algorithm can accurately find the Pareto fronts for a set of test functions. We then solve a bi-objective disc brake design problem, which indeed converges quickly.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/cb632ebf216b42ca53d14fb66924921b75b4a9a6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xingshi He
2. Iztok Fister
3. Xin-She Yang
4. Janez Brest
5. Mehmet Karamanoglu
6. Dusan Fister
7. N. Rajasekar
8. J. Prasanth Ram

In Civil and Structural Engineering

1. Xin-She Yang
2. Xingshi He
3. N. Rajasekar
4. J. Prasanth Ram
5. Mehmet Karamanoglu
6. Iztok Fister
7. Janez Brest
8. Yujun Zheng

In Computational Theory and Mathematics

1. Xingshi He
2. Mehmet Karamanoglu
3. Iztok Fister
4. Janez Brest
5. Xin-She Yang
6. N. Rajasekar
7. J. Prasanth Ram
8. Dusan Fister

Citations in the corpus

• Flower pollination algorithm: A novel approach for multiobjective optimization (2014, positive): “FPA has been extended to multi-objective optimization (Yang et al. 2013).”
• Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, background): “…literature, the benchmarking practice seems to use a set of test functions with differ-ent properties (such as mode shapes, separability and optima locality), there are many such benchmark test functions [Jamil and Yang(2013)] and some test suites designed by different conferences or research groups.”
• A Brief Review of Nature-Inspired Algorithms for Optimization (2013, methodology): “For example, the flower algorithm [8], or flower pollination algorithm [9], developed by Xin-She Yang in 2012, is a bio-inspired algorithm, but it is not an SI-based algorithm because the flower algorithm tries to mimic the pollination characteristics of flowering plants and the associated flower co”
• A Novel Flower Pollination Based Global Maximum Power Point Method for Solar Maximum Power Point Tracking (2017, methodology): “The shade in this case has led to have four different peaks with three LMPP occurring at 30, 51, and 49 W, respectively, and one GMPP at 55.7 W.”