Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
by Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga
15 Biogeography‐Based Optimization
Summary
This chapter describes the biogeography‐based optimization (BBO), which is inspired by the science of biogeography and a meta‐heuristic optimization algorithm. This chapter presents a brief literature review of the BBO and its applications and reviews the discipline of biogeography and its analogy to BBO. The BBO algorithm is described in detail, and a pseudocode of the BBO algorithm closes the chapter.
15.1 Introduction
Simon (2008) introduced the biogeography‐based optimization (BBO) algorithm utilizing biogeographic concepts. Savsani et al. (2014) studied the effect of hybridizing the BBO technique with artificial immune algorithm (AIA) and the ant colony optimization (ACO). Niu et al. (2014) proposed a BBO algorithm with mutation strategies (BBO‐M), which employs mutation motivated by the differential evolution (DE) algorithm and chaos theory for improving the global searching capability of the algorithm. Gupta et al. (2015) implemented the BBO for optimal component sizing of off‐grid small autonomous hybrid power systems (SAHPS) by minimizing the cost of energy. Yang (2015) proposed a modified biogeography‐based optimization (MBBO) algorithm to solve a flexible job shop scheduling problem (FJSSP). Tamjidy et al. (2015) used the BBO to deal with hole‐making process problem. Bozorg‐Haddad et al. (2015) used the BBO to optimal operation of single‐ and multi‐reservoir systems.