List of metaphor-based metaheuristics
This is a chronologically ordered list of metaphor-based metaheuristics and swarm intelligence algorithms, sorted by decade of proposal.
The ant colony optimization algorithm is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs.
From a broader perspective, ACO performs a model-based search[3] and shares some similarities with the estimation of distribution algorithms.
PSO is originally attributed to Kennedy, Eberhart and Shi[4][5] and was first intended for simulating social behaviour[6] as a stylized representation of the movement of organisms in a bird flock or fish school.
[10] Harmony search is a phenomenon-mimicking metaheuristic introduced in 2001 by Zong Woo Geem, Joong Hoon Kim, and G. V. Loganathan[11] and is inspired by the improvization process of jazz musicians.
The imperialist competitive algorithm (ICA), like most of the methods in the area of evolutionary computation, does not need the gradient of the function in its optimization process.
This algorithm starts by generating a set of random candidate solutions in the search space of the optimization problem.
This heuristic optimization method was proposed in 2007 by Rabanal et al.[31] The applicability of RFD to other NP-complete problems has been studied,[32] and the algorithm has been applied to fields such as routing[33] and robot navigation.
[41][42] In this way, human swarms can answer questions, make predictions, reach decisions, and solve problems by collectively exploring a diverse set of options and converging on preferred solutions in synchrony.
Invented by Dr. Louis Rosenberg in 2014, the ASI methodology has been noted for its ability to make accurate collective predictions that outperform the individual members of the swarm.
was challenged by a reporter to predict the winners of the Kentucky Derby; it successfully picked the first four horses, in order, beating 540 to 1 odds.
[44][45] Self-tuning metaheuristics have emerged as a significant advancement in the field of optimization algorithms in recent years, since fine tuning can be a very long and difficult process.
[46] These algorithms differentiate themselves by their ability to autonomously adjust their parameters in response to the problem at hand, enhancing efficiency and solution quality.
This self-tuning capability is particularly important in complex optimization scenarios where traditional methods may struggle due to rigid parameter settings.
[47][48] Kenneth Sörensen noted:[49] In recent years, the field of combinatorial optimization has witnessed a true tsunami of "novel" metaheuristic methods, most of them based on a metaphor of some natural or man-made process.
The behavior of virtually any species of insects, the flow of water, musicians playing together – it seems that no idea is too far-fetched to serve as inspiration to launch yet another metaheuristic.
Sörensen and Glover stated:[50] A large (and increasing) number of publications focuses on the development of (supposedly) new metaheuristic frameworks based on metaphors.
Ants, bees, bats, wolves, cats, fireflies, eagles, dolphins, frogs, salmon, vultures, termites, flies, and many others, have all been used to inspire a "novel" metaheuristic.
In response, Springer's Journal of Heuristics has updated their editorial policy to state:[51] Proposing new paradigms is only acceptable if they contain innovative basic ideas, such as those that are embedded in classical frameworks like genetic algorithms, tabu search, and simulated annealing.
The Journal of Heuristics avoids the publication of articles that repackage and embed old ideas in methods that are claimed to be based on metaphors of natural or manmade systems and processes.
These so-called "novel" methods employ analogies that range from intelligent water drops, musicians playing jazz, imperialist societies, leapfrogs, kangaroos, all types of swarms and insects and even mine blast processes (Sörensen, 2013).
[...] The Journal of Heuristics fully endorses Sörensen's view that metaphor-based “novel” methods should not be published if they cannot demonstrate a contribution to their field.
Their properties must be established on the basis of scientifically compelling arguments: mathematical proofs, controlled experiments, objective comparisons, etc.
