Abstract The “No Free Lunch” theorem states that, averaged over all optimization problems,
without re-sampling, all optimization algorithms perform equally well. Optimization, search …

Optimization in computationally expensive numerical problems with limited function
evaluations provides computational advantages over constraints based on runtime …

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution
of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of …

Memetic computation is a paradigm that uses the notion of meme (s) as units of information
encoded in computational representations for the purpose of problem-solving. It covers a …

Particle swarm optimization (PSO) has attracted much attention and has been applied to
many scientific and engineering applications in the last decade. Most recently, an intelligent …

This paper presents two hybrid differential evolution algorithms for optimizing engineering
design problems. One hybrid algorithm enhances a basic differential evolution algorithm …

In recent decades, many researchers have been interested in algorithms inspired by the
observation of natural phenomena to solve optimization problems. Among them, meta …

Memetic algorithms are optimization techniques based on the synergistic combination of
ideas taken from different algorithmic solvers, such as population-based search (as in …

Multimodal optimization, which aims at locating multiple optimal solutions within the search
space, is inherently a difficult problem. This work proposes an adaptive memetic differential …

Incremental social learning (ISL) was proposed as a way to improve the scalability of
systems composed of multiple learning agents. In this paper, we show that ISL can be very …