The self-organizing migrating algorithm (SOMA) is a population-based meta-heuristic that
belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the …

To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the
most challenging task. In this paper, performance of the various developed meta-heuristic …

This study proposes a technique aimed at the automatic search for parameter adaptation
strategies in a differential evolution algorithm with genetic programming symbolic …

This paper presents a new algorithm for optimizing parameters in swarm algorithm using
reinforcement learning. The algorithm, called iSOMA-RL, is based on the iSOMA algorithm …

In recent years, many improved particle swarm optimization (PSO) algorithms have been
developed to improve the performance of PSO. These improved algorithms have greatly …

A novel optimization technique is proposed based on ocean water currents. The
combination of all-natural forces determines ocean current speed, further when resultant of …

Heuristic algorithms are dependent on many coefficients like the number of iterations or
individuals. However, quite often these algorithms move individuals toward the best in the …

Benchmarking plays a vital role in evaluating and comparing evolutionary algorithms. Our
research addresses this critical need by introducing an innovative approach: rating-based …

This research paper aims to explore the possibilities of parameterization of state-of-the-art
adaptive mechanisms incorporated in the self-organizing migrating algorithm (SOMA). This …

This paper presents an experimental study that compares four adaptive variants of the self-
organizing migrating algorithm (SOMA). Each variant uses three different constraint handling …