This article presents a review and a comparative analysis between frameworks for solving
optimization problems using metaheuristics. The aim is to identify both the desirable …

This article presents a multi-agent framework for optimization using metaheuristics, called
AMAM. In this proposal, each agent acts independently in the search space of a …

Hyper-heuristic is a class of methodologies which automates the process of selecting or
generating a set of heuristics to solve various optimization problems. A traditional hyper …

We present a self-adaptive and distributed metaheuristic called Coalition-Based
Metaheuristic (CBM). This method is based on the Agent Metaheuristic Framework (AMF) …

Solving combinatorial optimization problems (COPs) poses a significant challenge in
various application domains. The NP-hardness of many COPs necessitates the integration …

In decision support systems, it is essential to get a candidate solution fast, even if it means
resorting to an approximation. This constraint introduces a scalability requirement with …

Multiobjective real parameter optimization is a challenging problem in majority of
engineering applications. This paper presents a creative multiagent and dynamic multi …

Solving combinatorial optimization problems is an important challenge in all engineering
applications. Researchers have been extensively solving these problems using evolutionary …

Real parameter optimization is an important task in almost all engineering applications. This
paper introduces a novel multiagent architecture and agent interaction mechanism for the …

The waiting time of passengers at the correspondence stations of buses is one of the most
important criteria to measure the service quality of transport. The mean of this work is to give …