A honeybees-inspired heuristic algorithm for numerical optimisation
نویسندگان
چکیده
منابع مشابه
Honeybees-inspired heuristic algorithms for numerical optimisation
Swarm BLOCKIN BLOCKIN intelligence BLOCKIN BLOCKIN is BLOCKIN BLOCKIN all BLOCKIN BLOCKIN about BLOCKIN BLOCKIN developing BLOCKIN BLOCKIN collective BLOCKIN BLOCKIN behaviours BLOCKIN BLOCKIN to BLOCKIN BLOCKIN solve BLOCKIN BLOCKIN complex, BLOCKIN BLOCKIN ill-‐structured BLOCKIN BLOCKIN and BLOCKIN BLOCKIN large-‐ scale problems. Efficiency in collective behaviours depends on how to harmon...
متن کاملA Meta-heuristic Algorithm for Global Numerical Optimization Problems inspired by Vortex in fluid physics
One of the most important issues in engineering is to find the optimal global points of the functions used. It is not easy to find such a point in some functions due to the reasons such as large number of dimensions or inability to derive them from the function. Also in engineering modeling, we do not have the relationships of many functions, but we can input and output them as a black box. The...
متن کاملthe algorithm for solving the inverse numerical range problem
برد عددی ماتریس مربعی a را با w(a) نشان داده و به این صورت تعریف می کنیم w(a)={x8ax:x ?s1} ، که در آن s1 گوی واحد است. در سال 2009، راسل کاردن مساله برد عددی معکوس را به این صورت مطرح کرده است : برای نقطه z?w(a)، بردار x?s1 را به گونه ای می یابیم که z=x*ax، در این پایان نامه ، الگوریتمی برای حل مساله برد عددی معکوس ارانه می دهیم.
15 صفحه اولA quantum inspired gravitational search algorithm for numerical function optimization
Gravitational search algorithm (GSA) is a swarm intelligence optimization algorithm that shares many similarities with evolutionary computation techniques. However, the GSA is driven by the simulation of a collection of masses which interact with each other based on the Newtonian gravity and laws of motion. Inspired by the classical GSA and quantum mechanics theories, this work presents a novel...
متن کاملHeuristic optimisation
1. The computational complexity of many problems means that optimal solutions are unlikely to be found in reasonable time in larger instances. 2. Problems may be ill-defined or data imprecise, so that an optimal solution based on estimated data will almost certainly not be optimal for the actual data. In such a situation it is preferable to obtain a robust solution that will be near-optimal ove...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Computing and Applications
سال: 2019
ISSN: 0941-0643,1433-3058
DOI: 10.1007/s00521-019-04533-x