Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding

Abstract

Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms ( HIOAs ). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms ( HIOAs ) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.

An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

“…2. Variety of swarm-based, math-based meta-heuristic algorithm has been hybridized with MPA and its variants to resolve different optimization problem however, in future one can even think of applying or integrating plant-based [119,127], human-based [8] and even physics/chemistry [47,68] based meta-heuristic algorithms to identify the potential of MPA and further progress the computational performance and generate quality solution. 3.…”

“…It involves the production of a set of assorted solutions at each run and the classification of population-based meta-heuristic algorithm into two main categories namely Evolutionary-Based and Nature-Inspired Algorithms [6,7]. Further, the nature-inspired algorithms are categorized into five different classes i.e., Swarm-Based, Physics/Chemistry-Based, Human-Based [8], Plant-Based and Maths-Based Algorithms and the same is depicted in Fig. 1.…”

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

“…As listed in Table 6 of Sect. 4.2, many swarm-based [239] and human-based meta-heuristic algorithms [240] have been combined with EO for different types of optimization problems. Further applications or integrations of other NIOA algorithm [241] types are possible, such as Plant-based, Maths-based or even Physics-Chemistrybased meta-heuristic algorithms [242,243], to determine the EO's potential, improve computational performance, and produce good solutions.…”

A Comprehensive Survey on Arithmetic Optimization Algorithm

“…In addition to that, the literature reports various NIOAs that have been produced up to the year 2022 that are based not just on natural laws but also on physical, social, and biological aspects. Under such circumstances, literature [1,3] has classified NIOAs into some heterogeneous classes (a) Evolutionary Algorithms (EA) [1,2]; (b) Bio-inspired optimization algorithms (BIOA) [1,2]; (c) Physics inspired optimization algorithms (PIOA) [5]; (d) Chemistry inspired optimization algorithms (CIOA) [4]; (e) Mathematics inspired optimization algorithms (MIOA) [4]; (f) Human inspired optimization algorithms (HIOA) [6]. The classification of NIOAs is represented as Fig.…”