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An ant algorithm is developed to solve the formulated problem.
Particle swarm optimization is used to find a near optimal solution for the formulated problem.
The fuzzy ant colony optimization (FACO) is also presented to solve the formulated problem.
The formulated problem is a combinational optimization as well as an NP-hard problem.
This paper proposes a novel Minimum Cost Maximum Power (MCMP) algorithm to solve the formulated problem.
Based on the covering linear programming (CLP), a fast iterative approximation scheme is designed to solve this newly formulated problem.
The formulated problem is a non-convex problem due to the non-concave objective function and also non-convex constraints.
The formulated problem consists of the minimization of the thermal surface area for a certain service, involving discrete decision variables.
We present the complete semi-analytical solution to the formulated problem in detail, describing the characteristic waves that may arise.
A main result claims that the formulated problem is reduced to a standard mixed H2/H∞ one for a linear finite dimensional time-invariant system.
By applying H∞ optimization techniques, a sufficient condition to solvability of the formulated problem is established in terms of Linear Matrix Inequalities.
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CEO of Professional Science Editing for Scientists @ prosciediting.com