A bilevel programming model represents the relationship in a specific decision process that
involves decisions within a hierarchical structure of two levels. The upper-level problem is …

In the past few decades, metaheuristics have demonstrated their suitability in addressing
complex problems over different domains. This success drives the scientific community …

In recent algorithmic family simulates different biological processes observed in Nature in
order to efficiently address complex optimization problems. In the last years the number of …

This paper introduces a new metaphor-free metaheuristic called attack-leave optimizer
(ALO). As the name suggests, ALO deploys two strategies to find the optimal solution. The …

A new metaheuristic can be developed by constructing from scratch, modifying the existing
metaheuristics, or hybridizing some metaheuristics. This work presents a new metaheuristic …

A novel metaheuristic algorithm is proposed in this paper, namely stochastic komodo
algorithm (SKA). This proposed algorithm is an improved version of Komodo mlipir algorithm …

Climate change is causing an increase in the frequency and severity of floods in various
regions globally, raising concerns about the efficacy of evacuation planning strategies that …

Population-based optimization algorithms are popular tools for optimization, influenced by
two interactive factors: landscape bias, which guides the population toward better objective …

This work offers a new stochastic optimization ie, a metaheuristic algorithm combining both
directionbased search and neighbourhood search called as walk-spread algorithm (WSA) …

Abstract Chaos Game Optimization (CGO) is a heuristic optimization approach that
estimates global optima for optimization problems using operators based on chaos theory …