In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn the northern United States and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called Monarch Butterfly Optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands viz. Southern Canada & northern United States (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness uations, the sum of the newly-generated but

MBO_FEs_v1\Agent.cpp
..........\Agent.h
..........\ggwFunc.cpp
..........\ggwFunc.h
..........\main.cpp
..........\MBO_Object.cpp
..........\MBO_Object.h
..........\output.txt
....Generations_v1\Agent.cpp
..................\Agent.h
..................\ggwFunc.cpp
..................\ggwFunc.h
..................\main.cpp
..................\MBO_Object.cpp
..................\MBO_Object.h
..................\output.txt
Readme.txt
MBO_FEs_v1
MBO_Generations_v1

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