Hybrid biogeography based optimization for constrained optimal spot color matching

Abstract

Biogeography-based optimization (BBO) is a new evolutionary algorithm which mimics the immigration and emigration of species among islands. Used widely in packaging and printing to obtain a colorful appearance, the spot color matching (SCM) is formulated as a complex multi-dimensional optimization problem. In this article, BBO is combined with the harmony search (HS) and opposition-based learning (OBL) approaches to construct an effective hybrid algorithm for solving the SCM problem. HS is used to enhance the local searching ability of BBO, and OBL is employed to increase the diversity of initial population; consequently, the exploration and exploitation abilities of the hybrid algorithm are enhanced and well balanced. Experiment results are presented to show the effectiveness of the proposed scheme.

A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem

“…In BBO (Simon 2008;Lin, Xu, and Zhang 2014;Lin 2015), each island is called a habitat (solution), and the goodness of the habitat is evaluated by the habitat suitability index (HSI). A solution with a high HSI value is of good quality, while a solution with a low HSI value is inferior in the optimisation problem.…”

“…BBO has demonstrated good performance when compared with other EAs (Bhattacharya and Chattopadhyay 2011; Jamuna and Swarup 2011;Ma and Simon 2011;Roy, Ghoshal, and Thakur 2011;Lin, Xu, and Zhang 2014;Wang et al 2014;Lin 2015). However, to the best of our knowledge, specific research conducted on BBO to solve the PFSP has not yet been attempted.…”

A hybrid method based on krill herd and quantum-behaved particle swarm optimization

“…In order to cope with this limitation, a variety of modern nature-inspired intelligent algorithms has been put forward and applied to solve optimization problems. Some of them, such as particle swarm optimization (PSO) [1][2][3][4], ant colony optimization (ACO) [5][6][7], bat algorithm (BA) [8][9][10][11][12], differential evolution (DE) [13][14][15], firefly algorithm (FA) [16][17][18], biogeographybased optimization (BBO) [19][20][21][22][23], cuckoo search (CS) [24][25][26][27][28], artificial bee colony (ABC) [29][30][31], ant lion optimizer (ALO) [32], multi-verse optimizer (MVO) [33], charged system search (CSS) [34][35][36], gravitational search algorithm (GSA) [37][38][39], animal migration optimization (AMO) [40], interior search algorithm (ISA) [41], grey wolf optimizer (GWO) [42,43], harmony search (HS) [44][45]…”