Chaotic Krill Herd algorithm

A survey on new generation metaheuristic algorithms

Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.

Feature selection via a novel chaotic crow search algorithm

An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems

Dwarf Mongoose Optimization Algorithm

A note on teaching-learning-based optimization algorithm

The main objective of this paper is to correct the unreasonable and inaccurate criticism to our previous experiments using Teaching---Learning-Based Optimization algorithm and to quantify the amount of error that may arise due to incorrect counting of fitness evaluations. It is shown that inexact experiment replication should be avoided in comparisons between meta-heuristic algorithms whenever possible. Otherwise, an inexact replication and margin of error should be explicitly reported.

Is a comparison of results meaningful from the inexact replications of computational experiments

Exploration, the process of visiting a new region in a search space, and exploitation, the process of searching in the neighborhood of previously visited regions, are two centerpieces of any metaheuristic algorithm. It is a common belief that good results can be obtained only if there is a good balance between exploration and exploitation. Hence, there is an urgent need to control the balance between exploration and exploitation in a direct manner. But, currently, direct measures of exploration and exploitation are almost non-existent, and researchers rely on indirect measures of exploration and exploitation, such as diversity, entropy, and fitness improvements. To remedy this situation, in this paper, a novel direct measure of exploration and exploitation is proposed that is based on attraction basins — parts of a search space where each part has its own point called an attractor, to which neighboring points tend to evolve. Each search point can be associated with a particular attraction basin. If a newly generated search point belongs to the same attraction basin as its parent then the search process is identified as exploitation, otherwise as exploration. In this paper, a new technique to compute attraction basins is presented, as well as a novel direct measure ( E x p B a s ) of exploration and exploitation based on attraction basins. On the selected set of unimodal and multimodal optimization problems it is shown that the newly proposed direct measure of exploration and exploitation is more accurate than our previously proposed direct measure ( E x p D i s t ), as well as the common indirect measure based on diversity (Diversity).

A novel direct measure of exploration and exploitation based on attraction basins

Necessary conditions for a domain $\Omega\subset \mathbb C^n$ admitting a local plurisubharmonic defining function on the boundary are given. In tandem, we give an algorithm to construct a local plurisubharmonic defining function on the boundary when one exists. In some cases we show that the necessary conditions are also sufficient.

Local Plurisubharmonic Defining Functions on the Boundary.

We prove that a singular real-analytic Levi-flat hypersurface $H$ in $\mathbb C^n$ being Segre-degenerate at a point $p$ is equivalent to the existence of a so-called support curve, that is, a holomorphic curve that intersects $H$ at exactly one point, which in turn is equivalent to the existence of support curves on at least two sides of $H$ at $p$. The existence of such two-sided support provides families of analytic discs attached to $H$ that covers a neighborhood of $p$. The existence of such discs has two corollaries. First, any function holomorphic on a neighborhood of a Segre-degenerate $H$ extends to a fixed neighborhood of $p$. Second, the rational hull of $H$ is a neighborhood of $p$, and thus no Levi-flat Segre-degenerate hypersurface in $\mathbb C^n$ can be rationally convex.

Luka Mernik

Papers

A note on teaching-learning-based optimization algorithm

Is a comparison of results meaningful from the inexact replications of computational experiments

A novel direct measure of exploration and exploitation based on attraction basins

Local Plurisubharmonic Defining Functions on the Boundary.

On Segre-degenerate Levi-flat hypervarieties