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Titlebook: Cohort Intelligence: A Socio-inspired Optimization Method; Anand Jayant Kulkarni,Ganesh Krishnasamy,Ajith Abr Book 2017 Springer Internati

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發(fā)表于 2025-3-23 11:44:46 | 只看該作者
Cohort Intelligence: A Socio-inspired Optimization Method
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發(fā)表于 2025-3-23 17:34:32 | 只看該作者
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發(fā)表于 2025-3-23 19:54:27 | 只看該作者
Andreas Weyland,Florian Jelschenan communicate with one another either directly or indirectly. The techniques such as Particle Swarm Optimization (PSO) is inspired from the social behavior of bird flocking and school of fish searching for food.
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發(fā)表于 2025-3-23 23:04:10 | 只看該作者
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發(fā)表于 2025-3-24 02:57:28 | 只看該作者
https://doi.org/10.1007/978-3-662-45148-9iciency, utilization with minimum initial investment and operational cost of various household as well as industrial equipments and machineries. To set a record in a race, for example, the aim is to do the fastest (shortest time).
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發(fā)表于 2025-3-24 08:46:24 | 只看該作者
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發(fā)表于 2025-3-24 20:22:22 | 只看該作者
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發(fā)表于 2025-3-25 02:05:46 | 只看該作者
,Solution to 0–1 Knapsack Problem Using Cohort Intelligence Algorithm,chapter further tests the ability of CI 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.
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