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The estimation of the optimal transformation is modelled either as a combinatorial or a numerical optimization problem.
In this computational study, a binary numerical optimization problem is formulated to achieve best mixing performance by identifying the optimal heterogeneous charge pattern.
We achieve general encodings of lenses by formulating the design as a numerical optimization problem.
In this work, the design of kinematic and dynamic parameters of links of a mobile manipulator is stated as a numerical optimization problem (NOP).
Control of chaotic systems to given targets is a subject of substantial and well-developed research issue in nonlinear science, which can be formulated as a class of multi-modal constrained numerical optimization problem with multi-dimensional decision variables.
In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem.
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The second experiment aims to evaluate and compare the performance of SA-MFO with MFO and five well-known optimization algorithms: particle swarm optimization (PSO) [15], ant bee colony (ABC) [14], moth flame optimization (MFO) [24], multi verse optimization (MVO) [23], and ant lion optimizer (ALO) [22] in solving 23 numerical optimization problems using different statistical measurements.
There are many evolutionary algorithms for numerical optimization problems.
Eight widely used benchmark numerical optimization problems are chosen from the literature as the test suit.
ACS is a swarm intelligence algorithm developed for solving numerical optimization problems.
In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems.
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