This article was published as a part of the Data Science Blogathon.
Introduction
Recently, many researchers have developed an interest in Nature-inspired Optimization Algorithms (NIOAs). This is because many of life’s challenges have been solved using simulations from life itself. Science has come up with some of the best inventions by simulating life. Drones have been developed by simulating birds. Deep Neural networks from simulating human neural networks, bee colony algorithms, and many others have been discovered in nature. Nature has proven to be very promising in simulating Technological innovations.
To offer effective and efficient answers to optimization problems in AI, a vast number of algorithms—about 120—and their variants have been presented. Nature has inspired many concepts, but NIOA is a field that sees algorithms and how new algorithms can be discovered from nature. Different surveys have been done to investigate NIOAs and their applications since it has proven to be a promising field.
What are NIOAs?
Nature‐inspired optimization algorithms (NIOA) are a set of algorithms that are illumed by the behavior of natural situations. NIOAs have been inspired by animal behaviors, biology, chemical reactions, etc. This has provided engineering solutions, medical solutions, etc.
Natural processes can easily be composed of several complicated sub-processes. This makes each of the algorithms to be unique and powerful. The study of NIOAs looks at making nature-inspired algorithms more effective by solving issues of selecting algorithms, tuning parameters, and updating old algorithms. Natural occurrences keep mutating, and the behaviors of algorithms need to adjust after a period of time. Therefore, we need to keep finding new ones and updating previous ones.
Some NIOAs include the genetic algorithm, Particle Swarm Optimization algorithm, Artificial Bee Colony algorithm, Cuckoo Search algorithm, Bat Algorithm, Firefly Algorithm, Immune Algorithm, Gray Wolf Optimizer, and so on.
Classification of NIOAs Algorithms
Nature-inspired algorithms can be grouped as stochastic and deterministic.
Stochastic/Randomized NIOAs; Stochastic refers to the property of being well described by a random probability distribution. It refers to a modeling approach. If an NIOA shows any form of randomness in its overall behavior, it is termed a stochastic or randomized NIOA.
Deterministic NIOAs; deterministic systems are systems in which no randomness has involved the production of outputs. The outputs can be known beforehand or expected. Yes, all experimental environments are always unknown, but sometimes, there is a range of expectations from the experiment. The values are fixed numbers for generating further solutions. We can say the values are easy to estimate.
Most NIOAs randomly get solutions from the search space, making them stochastic. Some others are in a more controlled condition and are hence, deterministic. The result may be a local optimum.
Structures and Behaviors of NIOAS
Research has shown that NIOAs have common behaviors. Knowing this can help scientists understand how to discover new types. To advance in this field, it becomes necessary to spot the similarities among them. The common process of most of NIOAs is divided into four basic steps.
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The first stage of NIOAs systems behaviors
Firstly, the parameters of the population are identified. Each NIOA has parameters that are initialized before starting the optimization process. The initial population is generated by random methods, ensuring that it covers as much solution space as possible. The values of the parameters are fixed when the optimization begins, which may likely change later in the process.
Specifying the population and its size is based on the requirements of the optimization. The optimization goals. The parameters tend to go through an iterative process later in the algorithm, but these conditions are set when the parameters are initiated at the first instance. Initialization with the parameters at step one shapes the algorithm and makes it performance controlled. If the results do not meet expectations, the parameters can be adjusted from the first stage. Modularizing these parameters will make it fewer to adjust.
The second stage of NIOAs systems behaviors
In step two, the initialization of the parameters of the population is first computed. The iteration begins and continues. This is the first determinant of if the parameters instantiated will meet goals. The terminating condition will be met later in the flow.
The third stage of NIOAs systems behaviors
Stage three corresponds to the output of the second stage. This stage is commonly responsible for performing the key responsibility and using operators and variables to switch values among parameters of the whole population, ensuring the system is controlled. As the parameter values are switched, the system keeps adjusting and updating, being better in trying to reach the goals. For instance, the Artificial bee colony will have parameters for abandonment criteria, number of employed bees, onlooker, and scout bees. Food source selection and food source position update will be the variables. This will be adjusted as the algorithm performs its execution.
The fourth stage of NIOAs systems behaviors
Step four is where the algorithm concludes its performance and yields a result. This may be satisfactory or not satisfactory. The cycle can start again by adjusting the parameters and values and re-running the algorithm.
Challenges of NIOAs
If you are considering further researching these promising fields, you may want to know an area to which you could contribute. We need to see the following challenges to know possible research areas. Improving an already working system is another way of discovering and making discoveries.
NIOA operations can be extremely intricate. Even though the NIOAs have been widely and successfully used in a variety of application domains, there are still difficult issues to solve.
Shortage of researchers
Though recently several researchers have emerged in the field, the diversity of NIOAs across many fields has resulted in an impediment. There are few discussions on this aspect. Only a few already existing benchmark tests suit specific optimization problems in NIOAs such as automatic parameter tuning. This has caused inadequate theoretical investigation, making it quite difficult to distinguish the characteristics of different NIOAs.
Biology and mathematics hardly matter
Most Bio NIOAs are low in the documentation and active researchers making many algorithms difficult to learn. This may be because most Computer scientists are more Mathematics and Physics inclined than Biology, yet biology holds the root of NIOAs. Researchers in the field require good knowledge of biological systems to imitate or improve previous systems. It is not common to find mathematics and biology working together; they are the requirements of the two fields in question.
It is also necessary to lay a solid foundation of mathematical theories to support NIOAs. These examples include a thorough understanding of time complexity, convergence proof, topological structures to understand various NIOAs, and a thorough analytical study of automatic parameter tuning techniques to address the parameter-dependence issue. Since practically all NIOAs operate in the black-box mode and lack a strong mathematical foundation, researchers are constantly proposing supposedly “new” algorithms and claiming that their optimizers find better solutions than those of other NIOAs.
Complexity
Simulations done in NIOAs are highly complex. This is because real environments are complicated, and the optimization problems can be multi-dimensional and large-scale with many variables and operators. Things in the real world can be changing and the optimization environments can be dynamic and uncertain. This could consume development time and resources. The complexity of the real environments poses a great challenge to NIOAs.
Conclusion
Nature-Inspired Optimization Algorithms (NIOAs) can provide satisfactory solutions to problems that are difficult and seem impossible for traditional optimization methods to address. Many NIOAs have been proposed in the last decades. And we have seen that it is a field to consider.
We lastly saw several challenges in the field. To solve all these problems, researchers could combine similar problems together. Merging NIOAs with other fields might expand the research grounds. Numerous optimization issues in the actual world are highly complicated. Creating more focused and efficient NIOAs for the aforementioned issues is wise. There are still a lot of issues with the research that has already been done, and a thorough review of the theoretical underpinnings of other NIOAs is required. Additionally, a benchmark test suite and user-friendly algorithm toolbox must be created for various issues, such as automatic parameter adjustment and the aforementioned issues in complicated situations.
Key takeaways;
- many researchers have developed an interest in Nature-inspired Optimization Algorithms (NIOAs). This is because many of life’s challenges have been given solutions using simulations from life itself
- Nature‐inspired optimization algorithms (NIOA) are a set of algorithms that are illumed by the behavior of natural situations.
- Nature-inspired algorithms can be grouped as stochastic and deterministic.
- The common process of most of NIOAs is divided into four basic steps.
- Even though the NIOAs have been widely and successfully used in a variety of application domains, there are still difficult issues to solve, including a Shortage of researchers, Biology and Mathematics differences, and Complexity
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I am an AI Engineer with a deep passion for research, and solving complex problems. I provide AI solutions leveraging Large Language Models (LLMs), GenAI, Transformer Models, and Stable Diffusion.
