Book Chapter10.1007/978-981-13-2586-1_2
Biogeography-Based Optimization
Yu-Jun Zheng,Xueqin Lu,Min-Xia Zhang,Shengyong Chen +3 more
- 01 Jan 2019
- pp 27-49
29
TL;DR: This chapter introduces the basic BBO and its recent advances for constrained optimization, multi-objective optimization, and combinatorial optimization.
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Abstract: Biogeography is a discipline of the distribution, migration, and extinction of biological populations in habitats. Biogeography-based optimization (BBO) is a heuristic inspired by biogeography for optimization problems, where each solution is analogous to a habitat with an immigration rate and an emigration rate. BBO evolves a population of solutions by continuously migrating features probably from good solutions to poor solutions. This chapter introduces the basic BBO and its recent advances for constrained optimization, multi-objective optimization, and combinatorial optimization.
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Citations
Solving the associated weakness of biogeography-based optimization algorithm
TL;DR: Two modifications are proposed; modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands and the clear duplication process, located after the mutation stage, is sized to avoid any corruption on the suitability index variables of the non-mutated islands.
Biogeography Based Optimization Approach for Solving Optimal Power Flow Problem
TL;DR: A novel evolutionary algorithm called Biogeography-based optimization (BBO) has been applied to solve the optimal power flow problems on IEEE 30-bus test system with six generating units to test the effectiveness of the proposed method.
Investigating Performance of Various Natural Computing Algorithms
TL;DR: An investigation by assessing the performance of some of the well-known natural computing algorithms with their variations, including Genetic Algorithm, Ant Colony Optimization, River Formation Dynamics, Firefly Algorithm and Cuckoo Search to establish the superiority of Firefly Algorithms over the other algorithms in comparative terms.
ARAe-SOM+BCO: An enhanced artificial raindrop algorithm using self-organizing map and binomial crossover operator
TL;DR: The experiment results show that the performance of ARA e -SOM+BCO significantly outperforms ARA and its extension variant, and is also competitive with other state-of-the-art evolutionary algorithms in most test functions.
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References
Genetic algorithms in search, optimization and machine learning
David E. Goldberg
- 01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
58.6K
•Book
Genetic algorithms in search, optimization, and machine learning
David E. Goldberg
- 01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
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