A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Abstract

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

“…Figure 10 depicts this abbreviated taxonomy. A gentle introduction with more in-depth details on this topic can be found in [248]. Some prominent Swarm Intelligence Algorithms include Particle Swarm Optimization [249], Ant Colony Optimization [250], Gray Wolf Optimizer [251], Whale Optimization Algorithm [252], Bat Algorithm [253], Firefly Algorithm [254], Cuckoo Search Algorithm [255], Orca Predation Algorithm [256], Starling Murmuration Optimizer [257], etc.…”

Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap

“…The Simulated Annealing method enhances alignment in MSA [27,28]. The Gibbs sampling technique has successfully identified barriers in local multiple alignments without gaps; however, dealing with gapped alignments posed challenges in their endurance and reproduction [5]. The best score of the goal function determines the fitness of an alignment.…”

“…The application of bioinspired techniques in Multiple Sequence Alignment (MSA) has garnered considerable attention in the field of bioinformatics, driven by the proliferation of recent scholarly works [31][32][33]. Bioinspired algorithms possess robust search capabilities, rendering them well-suited for addressing optimization challenges like MSA [5,34,35]. The initial phase entails preparing the sequences for alignment, which encompasses tasks such as sequence preprocessing, selection, and weighting.…”

“…While traditional algorithms, such as dynamic programming-based methods [3,4] can align a limited number of sequences, they struggle with the immense amounts of sequence data generated by modern sequencing technologies. As a result, researchers have turned to innovative, bioinspired algorithms [5] as a potential solution to the challenges of computational efficiency and scalability faced by conventional methods [6].…”

A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems

“…The optimization issues are stated mathematically in three parts: objective function, constraints, and decision variables [ 1 ]. In the research of optimization, problem solution strategies are classified as deterministic or stochastic [ 2 ]. Stochastic techniques solve optimization issues by randomly exploring the searching space and employing arbitrary operators.…”