From a76ce537bc177479e050f2151437337ef06cd8b8 Mon Sep 17 00:00:00 2001 From: Somyajit Chakraborty Sam Date: Mon, 17 Feb 2020 17:27:29 +0530 Subject: [PATCH] Update README.md --- README.md | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index feaedc3..5d10b73 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,10 @@ architecture. An ant colony can be thought of as a swarm whose individual agents Similarly a flock of birds is a swarm of birds. An immune system is a swarm of cells and molecules as well as a crowd is a swarm of people . Particle Swarm Optimization (PSO) Algorithm models the social behaviour of bird flocking or fish schooling .
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PROPOSED APPROACH

In this work, a particular intelligent behaviour of a honey bee swarm, foraging behaviour, is considered and a new artificial bee colony (ABC) algorithm simulating this behaviour of real @@ -22,7 +26,7 @@ the second half includes the onlookers. For every food source, there is only one bee. In other words, the number of employed bees is equal to the number of food sources around the hive. The employed bee whose food source has been exhausted by the bees becomes a scout. The main steps of the algorithm are given below:
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Algorithm:


Send the scouts onto the initial food sources
@@ -63,4 +67,11 @@ the optimal solutions of the difficult optimization problems can be discovered. and progress of the real bee swarm depended upon the rapid discovery and efficient utilization of the best food resources. Similarly the optimal solution of difficult engineering problems is connected to the relatively fast discovery of “good solutions” especially for the problems that -need to be solved in real time. +need to be solved in real time.
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