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10.1109/TEVC.2020.2992387

Most evolutionary many-objective optimization (EMaO) algorithms start with a description of a number of the predefined set of reference points on a unit simplex.

Generating Well-Spaced Points on a Unit Simplex for Evolutionary Many-Objective Optimization

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10.1007/s10589-021-00307-1

The paper deals with the numerical solution of the problem P to maximize a homogeneous polynomial over the unit simplex.

Two methods for the maximization of homogeneous polynomials over the simplex

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10.1109/CEC45853.2021.9504903

Numerical results from evaluating performance on synthetic solutions on a unit simplex plane and benchmark functions of MaF show that the proposed performance indicator IRNA is more sensitive to capturing diversity changes as the number of objectives increases compared to other popular indicators.

A New Diversity Performance Indicator for Many-Objective Optimisation Problems

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10.1007/s40435-021-00755-x

The system’s matrices linearly depend on uncertain parameters which belong to the unit simplex known as the uncertain space.

Stability analysis of linear time-invariant systems in the presence of polytopic uncertainty and a time delay state

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10.1109/TIFS.2021.3052356

Motivated by such concerns, we introduce a novel mechanism that privatizes vectors belonging to the unit simplex.

Differential Privacy on the Unit Simplex via the Dirichlet Mechanism

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10.1109/CEC45853.2021.9504769

Well-known MOEAs adopt the generation of uniformly distributed points on a unit simplex to construct such reference sets.

Towards a More Balanced Reference Set Adaptation Method: First Results

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10.1007/S10898-021-01017-Y

A standard quadratic program is an optimization problem that consists of minimizing a (nonconvex) quadratic form over the unit simplex.

Global solutions of nonconvex standard quadratic programs via mixed integer linear programming reformulations

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Unit Simplex - Most evolutionary many-objective optimization (EMaO) algorithms start with a description of a number of the predefined set of reference points on a unit simplex. [1] The paper deals with the numerical solution of the problem P to maximize a homogeneous polynomial over the unit simplex. [2] Numerical results from evaluating performance on synthetic solutions on a unit simplex plane and benchmark functions of MaF show that the proposed performance indicator IRNA is more sensitive to capturing diversity changes as the number of objectives increases compared to other popular indicators. [3] The system’s matrices linearly depend on uncertain parameters which belong to the unit simplex known as the uncertain space. [4] Motivated by such concerns, we introduce a novel mechanism that privatizes vectors belonging to the unit simplex. [5] Well-known MOEAs adopt the generation of uniformly distributed points on a unit simplex to construct such reference sets. [6] A standard quadratic program is an optimization problem that consists of minimizing a (nonconvex) quadratic form over the unit simplex. [7]