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Population-based optimization algorithms motivated from nature commonly locate near-optimal solution to optimization problems.
However, the so-obtained optimal solution to optimization problem (8) probably results in poor properties, e.g. poor range resolution and high sidelobe levels.
A meta-heuristic is a higher level procedure that can be used to find a near global optimal solution to optimization problems that include incomplete or imperfect information or limited computation capacity.
Set the antibody as a viable solution to optimization problems.
Therefore, a precise solution to optimization (20) can be achieved by solving (29) if and only if a very small τ is used in (28).
Multipath routing for Opnet is achieved by statically adding the routes that have been achieved through solution to optimization model for the specific network instance.
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This heuristic is routinely used to generate useful solutions to optimization and search problems.
Particle swarm optimization (PSO) is a metaheuristic designed to find good solutions to optimization problems.
Heuristic algorithms seek "acceptable" solutions to optimization challenges by "trail-and-error" taking a "reasonable" amount of computational timing.
GAs generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.
This method can sometimes be used to find efficient solutions to optimization problems which ask us to find an object which minimizes or maximizes a certain quantity from a range of possible solutions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com