Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

With the explosion of data generation, getting optimal solutions to data driven problems is
increasingly becoming a challenge, if not impossible. It is increasingly being recognised that …

This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm
(AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics …

This study introduces the extension of currently developed Seagull Optimization Algorithm
(SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull …

In this paper, a novel population-based, nature-inspired optimization paradigm is proposed,
which is called Harris Hawks Optimizer (HHO). The main inspiration of HHO is the …

Moth-flame optimization (MFO) is a widely used nature-inspired algorithm characterized by a
simple structure with simple parameters. However, for some complex optimization tasks …

Multiobjective reliability-based design optimization (RBDO) is a research area, which has
not been investigated in the literatures comparing with single-objective RBDO. This work …

This paper proposes a novel population-based optimization algorithm called Sine Cosine
Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random …

Abstract Recently developed Harris Hawks Optimization has virtuous behavior for finding
optimum solution in search space. However, it easily get trapped into local search space for …

Crack detection is a crucial task in periodic pavement survey. This study establishes and
compares the performance of two intelligent approaches for automatic recognition of …