Termite colony optimization: A novel approach for optimizing continuous problems

Hedayatzadeh, Ramin; Salmassi, Foad Akhavan; Keshtgari, Manijeh; Akbari, Reza; Ziarati, Koorush

doi:10.1109/iraniancee.2010.5507009

Cited by 63 publications

(13 citation statements)

References 8 publications

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“…In fact, the mid‐2000s turned out to be a time so plentiful of “novel” metaphors as to make the Cambrian explosion pale in comparison. Just in the area of social insects, “novel” algorithms were introduced involving ants, honey bees (Karaboga, ), flies (Abidin et al., ), fruit flies (Pan, ), termites (Hedayatzadeh et al., ), fireflies (Łukasik and Żak, ), glow worms (Krishnanand and Ghose, ), and probably some other that the author is unaware of. The differences between these various social insect algorithms proved marginal at best.…”

Section: History Of Metaphors In Metaheuristics Researchmentioning

confidence: 99%

Metaheuristics—the metaphor exposed

Sörensen

2013

Int Trans Operational Res

809

346

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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. In this paper, we will argue that this line of research is threatening to lead the area of metaheuristics away from scientific rigor. We will examine the historical context that gave rise to the increasing use of metaphors as inspiration and justification for the development of new methods, discuss the reasons for the vulnerability of the metaheuristics field to this line of research, and point out its fallacies. At the same time, truly innovative research of high quality is being performed as well. We conclude the paper by discussing some of the properties of this research and by pointing out some of the most promising research avenues for the field of metaheuristics.The infamous Higgs boson that gives other particles their mass is, of course, the "salt" particle in the new theory, which gives other "dishes" their "flavor."There is not much doubt that the reaction of the scientific community over this article would be one of ridicule, if not outrage. In letters to the editor (who would probably be fired for allowing this article to be published), the question would rightfully be asked whether any new contribution was made other than a re-iteration of existing knowledge. The authors would be widely criticized for their attempt to change the vocabulary of the standard theory of particle physics, a tool that has been instrumental in allowing scientists across the world to communicate unambiguously. Steps would certainly be taken for this type of "research" never to be published again in a reputable journal.The above story may strike the reader as very unlikely, yet it is not as far-fetched as it may seem at first sight. On the contrary, in the research field of optimization using metaheuristics, contributions akin to the hypothetical paper described above are frighteningly common. For a few decades, every year has seen the publication of several papers claiming to present a "novel" method for optimization, based on a metaphor of a process that is often seemingly completely unrelated to optimization. The jumps of frogs, the refraction of light, the flowing of water to the sea, an orchestra playing, sperm cells moving to fertilize an egg, the spiraling movements of galaxies, the colonizing behavior of empires, the behavior of bats, birds, ants, bees, flies, and virtually every other species of insects -it seems that there is not a single natural or man-made process that cannot be used as a metaphor for yet another "novel" optimization method. Invariably, the authors of such papers promise that their "new" method is superior to methods previously published in the literature. Rather than being scorned for the reasons mentioned above, mor...

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Section: History Of Metaphors In Metaheuristics Researchmentioning

confidence: 99%

Metaheuristics—the metaphor exposed

Sörensen

2013

Int Trans Operational Res

809

346

View full text Add to dashboard Cite

show abstract

“…While they move in the solution space of fitness function, the particles aim to improve their next position based on their past experience and the best position in the swarm. Therefore, every individual is gravitated toward a stochastically weighted average of the previous best position of its own and that of its neighborhood companions [30]. In every iteration of PSO, the position and velocity of every particle is updated and the value of fitness function at its current location is evaluated.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Dynamic economic dispatch using hybrid metaheuristics

Santra¹,

Mukherjee²,

Sarker³

et al. 2020

Journal of Electrical Systems and Inf Technol

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Dynamic economic dispatch problem or DED is an extension of static economic dispatch problem or SED which is used to determine the generation schedule of the committed units so as to meet the predicted load demand over a time horizon at minimum operating cost under ramp rate constraints and other constraints. This work presents an efficient hybrid method based on particle swarm optimization (PSO) and termite colony optimization (TCO) for solving DED problem. The hybrid method employs PSO for global search and TCO for local search in an interleaved mode towards finding the optimal solution. After the first round iteration of local search by TCO, the best local solutions are considered by PSO to update the schedules globally. In the next round, TCO performs local search around each solution found by PSO. This paper reports the methodology and result of application of PSO-TCO hybrid to 5-unit, 10-unit and 30-unit power dispatch problems; the result shows that the PSO-TCO (HPSTCO) gives improved solution compared to PSO or TCO (when applied separately) and also other hybrid methods.

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“…The term Swarm Intelligence (SI), first appeared in the late 80's of the last century [14], the SI algorithms are based on a group of simple agents that interact between them and their environment, this with the objective of achieving a cooperative behavior and more complex than what each agent could individually achieve. These algorithms are mainly inspired by natural phenomena such as the colonies of Ants [15], bees [16] or bacteria, water droplets [17], the behavior of bats [18], termites [19], among others.…”

Section: Swarm Intelligencementioning

confidence: 99%

Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

Cabrera-Hernandez¹,

Aceves-Fernández²,

Ramos-Arreguín³

et al. 2019

IJIS

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The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the PM10 particle. In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were carried with BFOA parameters, tuning them to achieve the best configuration of said parameters that produce an optimized model, demonstrating that way, how the optimization process is influenced by choice of the parameters. parameters individually influences said results.The methodology is basically to use BFOA as optimizer of a base model generated with another technique, ANFIS. The model generated with ANFIS presents some inaccuracies since it is unstable with highly non-linear problems such as the one that is to be modeled in this work. This is why this method was devised where the accuracy of the base model is improved. Once the model optimized with BFOA is generated, it will be compared against that generated with ANFIS.The use of an algorithm that has several agents or swarm intelligence, such as BFOA, gives us the opportunity to find an optimal solution since it involves several, relatively simple agents exploring the study area, thus having a greater probability of finding the optimal global values avoiding the problem of getting stuck in a local solution as it happens with other methods such as neural networks, as well as being robust, flexible systems without central control that issues orders to system agents [1].To better understand what the problem is, it is important to define some concepts, which are presented below.

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Termite colony optimization: A novel approach for optimizing continuous problems

Cited by 63 publications

References 8 publications

Metaheuristics—the metaphor exposed

Metaheuristics—the metaphor exposed

Dynamic economic dispatch using hybrid metaheuristics

Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

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