N-Queens Problem Solving using Apiary Organizational-Based Optimization Algorithm

Complex huge-scale scientific applications are simplified by workflow to execute in the cloud environment. The cloud is an emerging concept that effectively executes workflows, but it has a range of issues that must be addressed for it to progress. Workflow scheduling using a nature-inspired metaheuristic algorithm is a recent central theme in the cloud computing paradigm. It is an NP-complete problem that fascinates researchers to explore the optimum solution using swarm intelligence. This is a wide area where researchers work for a long time to find an optimum solution but due to the lack of actual research direction, their objectives become faint. Our systematic and extensive analysis of scheduling approaches involves recently high-cited metaheuristic algorithms like Genetic Algorithms (GA), Whale Search Algorithm (WSA), Ant Colony Optimization (ACO), Bat Algorithm, Artificial Bee Colony (ABC), Cuckoo Algorithm, Firefly Algorithm and Particle Swarm Optimization (PSO). Based on various parameters, we do not only classify them but also furnish a comprehensive striking comparison among them with the hope that our efforts will assist recent researchers to select an appropriate technique for further undiscovered issues. We also draw the attention of present researchers towards some open issues to dig out unexplored areas like energy consumption, reliability and security for considering them as future research work.

The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems

The paper reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including Firefly algorithm, PSO algorithms and ABC algorithm. By implementing these algorithms in Matlab, we will use worked examples to show how each algorithm works. Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by the flashing behaviour of fireflies in nature. There are many noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. Mathematical optimization or programming is the study of such planning and design problems using mathematical tools. Nowadays, computer simulations become an indispensable tool for solving such optimization problems with various efficient search algorithms. Nature-inspired algorithms are among the most powerful algorithms for optimization. Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behaviour of fireflies. A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, which simulates the flash pattern and characteristics of fireflies. It is a powerful swarm intelligence algorithm inspired by the flash phenomenon of the fireflies. In this context, three types of meta-heuristics called Artificial bee Colony algorithm, Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. A series of computational experiments using each algorithm were conducted. The stimulation result of this experiment were analyzed and compared to the best solutions found so The Firefly algorithm in each noisy non linear optimization function seems to perform better and efficient.

Kalman filtering and sine arithmetic optimization algorithm (KSAOA) for wireless sensor network clustering

Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

The metaheuristic algorithm is a very important area of research that continuously improves in solving optimization problems. Nature-inspired is one of the metaheuristic algorithm classifications that has grown in popularity among researchers over the last few decades. Nature-inspired metaheuristic algorithms contribute significantly to tackling many standing complex problems (such as the combinatorial t-way testing problem) and achieving optimal results. One challenge in this area is the combinatorial explosion problem, which is always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation has been selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-Time Hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization, whilst others were not. This research paper outlines a hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021), such as the Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out an up-to-date review of these distinguished algorithms with their respective current states of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics have a simple structure that can be adopted in different areas to become more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters settings to achieve an optimal solution.

A comprehensive survey of honey badger optimization algorithm and meta-analysis of its variants and applications

Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems, yet existing methods often struggle with balancing exploration and exploitation across diverse problem landscapes. This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization (PWO) algorithm. The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves, also known as African wild dogs in the wild, particularly their unique consensus-based voting rally mechanism, a behavior fundamentally distinct from the social dynamics of grey wolves. In this innovative process, pack members explore different areas to find prey; then, they hold a pre-hunting voting rally based on the alpha member to determine who will begin the hunt and attack the prey. The efficiency of the proposed PWO algorithm is evaluated by a comparison study with other well-known optimization algorithms on 33 test functions, including the Congress on Evolutionary Computation (CEC) 2017 suite and different real-world engineering design cases. Furthermore, the algorithm’s performance is further tested across a spectrum of optimization problems with extensive unknown search spaces. This includes its application within the field of cybersecurity, specifically in the context of training a machine learning-based intrusion detection system (ML-IDS), achieving an accuracy of 0.90 and an F-measure of 0.9290. Statistical analyses using the Wilcoxon signed-rank test (all p<0.05) indicate that the PWO algorithm outperforms existing state-of-the-art algorithms, providing superior solutions in diverse and unpredictable optimization landscapes. This demonstrates its potential as a robust method for tackling complex optimization problems in various fields. The source code for the PWO algorithm is publicly available at https://github.com/saeidsheikhi/Painted-Wolf-Optimization.

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo .

Problem Metaheuristics are efficient algorithms designed to address a broad spectrum of optimization challenges and offer satisfactory solutions, even in scenarios of limited processing capability or incomplete information. It has been observed that no single metaheuristic algorithm is universally ideal for all applications. This realization underscores the opportunity for the introduction of new metaheuristic algorithms or enhancements to existing ones. Aim The aim of this work is to propose Quokka swarm optimization (QSO), a novel nature-inspired metaheuristic optimization technique. The QSO simulates the cooperative behavior of quokka animals, which can be used to address optimization issues. Method A group of common unconstrained and constrained test functions is employed to demonstrate the strength of the proposed approach. To test the performance of QSO, 43 popular test functions that are used in the optimization were employed as benchmarks. The solutions have been refining their positions in tandem with the ongoing discovery of the best solution. In addition, QSO can substitute the worst quokka with the best child found so far to improve the solutions. Performance comparisons using the Blue monkey swarm optimization, Gray wolf optimization, Biogeography-based optimizer, Artificial bee colony, Particle swarm optimization, and Gravitational search algorithm were also performed. Results The obtained results showed that QSO is competitive in comparison to the chosen metaheuristic algorithms.

Over the years, Metaheuristic algorithms have proven significant efficacy in handling None- Deterministic polynomial hard (NP-hard) combinatorial optimization problems, mainly the Traveling Salesman Problem (TSP) which has been used to evaluate and test many metaheuristics algorithms such as Ant Colony optimizations. This paper contributes a new optimization algorithm to the famous-family of swarm and evolutionary techniques named Scuba Diver Optimization Algorithm (SDOA) which is a novel population-based metaheuristic optimization algorithm inspired by the behavioural and physiological dynamics of scuba divers in the open water environments. SDOA simulates main diving factors including (depth, tank pressure/oxygen, bottom time, and ascent strategies) for keeping a balance between explorations and exploitations during the search process. SDOA novelty appears in its adaptive control of solution diversity using pressure and oxygen level thresholds, enabling robust avoidance of local optima and faster convergence. The SDOA algorithm was evaluated on a range of TSP instances from the TSPLIB library, which were categorized into three groups: Group 1, comprising instances with 1–99 cities; Group 2, including instances with 100–999 cities; and Group 3, consisting of large-scale instances with 1000 or more cities. And the results of SDOA is compared with established metaheuristics algorithms such as Ant Colony Optimization, Artificial Bee Colony, and Genetic Algorithm. The study results showing that SDOA consistently produces shorter (near optimal) tour lengths with significantly minimum computational time in larger problem sizes (Group 3). The proposed algorithm also demonstrates better scalability and stability in different initialization and population sizes. Future directions include adapting SDOA for solving other optimization problems including multi-objective or dynamic optimization contexts.

Discrete tree seed algorithm for urban land readjustment

Atom search optimization algorithm based hybrid antenna array receive beamforming to control sidelobe level and steering the null

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images