As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community. 

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An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA. 

/pdf/nature-inspired-optimization-algorithms-and-their-uxb2g0vl.pdf

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Meta-heuristic search algorithms in truss optimization: Research on stability and complexity analyses

Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: "Proceedings of the Congress" (33, 10.09%), "Applied Soft Computing" (19, 5.81%), "Expert Systems with Applications" (18, 5.51%), "Knowledge-Based Systems" (12, 3.67%), "Engineering Applications of Artificial Intelligence" (12, 3.67%), "Advances in Engineering Software" (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece. 

Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022)

The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the Coati optimization algorithm (CoatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mCoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mCoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law Algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mCoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mCoatiOA are significantly different from those of the other rival algorithms. mCoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method. The proposed mCoatiOA achieved better results in comparison with other published methods. It achieved the mean best results on 75% of the datasets. 

An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization

This paper proposes a novel metaheuristic Child Drawing Development Optimization (CDDO) algorithm inspired by the child's learning behaviour and cognitive development using the golden ratio to optimize the beauty behind their art. The golden ratio was first introduced by the famous mathematician Fibonacci. The ratio of two consecutive numbers in the Fibonacci sequence is similar, and it is called the golden ratio, which is prevalent in nature, art, architecture, and design. CDDO uses golden ratio and mimics cognitive learning and child's drawing development stages starting from the scribbling stage to the advanced pattern-based stage. Hand pressure width, length and golden ratio of the child's drawing are tuned to attain better results. This helps children with evolving, improving their intelligence and collectively achieving shared goals. CDDO shows superior performance in finding the global optimum solution for the optimization problems tested by 19 benchmark functions. Its results are evaluated against more than one state of art algorithms such as PSO, DE, WOA, GSA, and FEP. The performance of the CDDO is assessed, and the test result shows that CDDO is relatively competitive through scoring 2.8 ranks. This displays that the CDDO is outstandingly robust in exploring a new solution. Also, it reveals the competency of the algorithm to evade local minima as it covers promising regions extensively within the design space and exploits the best solution.

Child Drawing Development Optimization Algorithm based on Child's Cognitive Development

This paper proposes a novel metaheuristic Child Drawing Development Optimization (CDDO) algorithm inspired by the child's learning behavior and cognitive development using the golden ratio to optimize the beauty behind their art. The golden ratio was first introduced by the famous mathematician Fibonacci. The ratio of two consecutive numbers in the Fibonacci sequence is similar, and it is called the golden ratio, which is prevalent in nature, art, architecture, and design. CDDO uses golden ratio and mimics cognitive learning and child's drawing development stages starting from the scribbling stage to the advanced pattern-based stage. Hand pressure width, length and golden ratio of the child's drawing are tuned to attain better results. This helps children with evolving, improving their intelligence and collectively achieving shared goals. CDDO shows superior performance in finding the global optimum solution for the optimization problems tested by 19 benchmark functions. Its results are evaluated against more than one state-of-art algorithms such as PSO, DE, WOA, GSA, and FEP. The performance of the CDDO is assessed, and the test result shows that CDDO is relatively competitive through scoring 2.8 ranks. This displays that the CDDO is outstandingly robust in exploring a new solution. Also, it reveals the competency of the algorithm to evade local minima as it covers promising regions extensively within the design space and exploits the best solution.

/pdf/child-drawing-development-optimization-algorithm-based-on-5546b001h7.pdf

Child Drawing Development Optimization Algorithm based on Child’s Cognitive Development

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Child Drawing Development Optimization Algorithm based on Child's Cognitive Development

Child Drawing Development Optimization Algorithm based on Child’s Cognitive Development