Cohort Intelligence Research Articles

Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm

An emerging technique, inspired from the natural and social tendency of individuals to learn from each other referred to as Cohort Intelligence (CI) is presented. Learning here refers to a cohort candidate’s effort to self supervise its own behavior and further adapt to the behavior of the other candidate which it intends to follow. This makes every candidate improve/evolve its behavior and eventually the entire cohort behavior. This ability of the approach is tested by solving an NP-hard combinatorial problem such as Knapsack Problem (KP). Several cases of the 0–1 KP are solved. The effect of various parameters on the solution quality has been discussed.The advantages and limitations of the CI methodology are also discussed.

A hybrid approach for data clustering based on modified cohort intelligence and K-means

Clustering is an important and popular technique in data mining. It partitions a set of objects in such a manner that objects in the same clusters are more similar to each another than objects in the different cluster according to certain predefined criteria. K-means is simple yet an efficient method used in data clustering. However, K-means has a tendency to converge to local optima and depends on initial value of cluster centers. In the past, many heuristic algorithms have been introduced to overcome this local optima problem. Nevertheless, these algorithms too suffer several short-comings. In this paper, we present an efficient hybrid evolutionary data clustering algorithm referred to as K-MCI, whereby, we combine K-means with modified cohort intelligence. Our proposed algorithm is tested on several standard data sets from UCI Machine Learning Repository and its performance is compared with other well-known algorithms such as K-means, K-means++, cohort intelligence (CI), modified cohort intelligence (MCI), genetic algorithm (GA), simulated annealing (SA), tabu search (TS), ant colony optimization (ACO), honey bee mating optimization (HBMO) and particle swarm optimization (PSO). The simulation results are very promising in the terms of quality of solution and convergence speed of algorithm.

Arts and artisans at home and abroad: with sketches of the progress of foreign manufactures

With the innovative development of advanced technology in the field of medical, there is an enlargement in the generation of other problems such as management of biomedical waste. Hazardous waste generated from hospitals is required to be managed within time and it can be done effectively using some computer science technology. In the proposed methodology, Biomedical Waste (BMW) problem is solved with the consideration of route optimization. Route optimization is important in BMW management because while transporting the BMW from hospital to depot (disposal site) there are many types of risks associated with that route like traffic, vehicle failure, road accident etc. To avoid the dangerous effects of BMW on humans and environment, it is necessary to optimize the distance. It can help in promoting healthy and risk free life. This paper addresses the problem of finding the shortest path using Cohort Intelligence algorithm for BMW management with the consideration of human risk.