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• The different variants of firefly algorithm are classified and analyzed to summarize the latest research and developments related to nature-inspired computing in the most general sense. • This review has proved that firefly algorithms can be practically applied within every problem domain. Moreover, this review has presented to researchers new technological trends and design challenges they have to cope with, while designing systems using this algorithm. Firefly algorithm (FA) is one of the popular algorithms of Swarm Intelligence domain. The literature has expanded significantly in the past few years. This paper provides a timely review of the FA and its new variants. The different variants of FA are classified and analyzed. The applications and case studies in the rapidly evolving domain are also reviewed and summarized in detail here. The problems to be further solved and future research directions of firefly algorithms will be given at the end of this paper. [ABSTRACT FROM AUTHOR]

• A hybridization between Moth-Flame Optimization and Marine Predators Algorithms (MOMPA) is proposed • The proposed MOMPA is employed for solving the robot path planning problem with obstacle avoidance constraints. • After the validation experiment, MOMPA is applied for a case study on New Galala city in Egypt. New cities exploit the smartness of the IoT-based architecture to run their vital and organizational processes. The smart response of pandemic emergency response services needs optimizing methodologies of caring and limit infection without direct connection with patients. In this paper, a hybrid Computational Intelligence (CI) algorithm called Moth-Flame Optimization and Marine Predators Algorithms (MOMPA) is proposed for planning the COVID-19 pandemic medical robot's path without collisions. MOMPA is validated on several benchmarks and compared with many CI algorithms. The results of the Friedman Ranked Mean test indicate the proposed algorithm can find the shortest collision-free path in almost all test cases. In addition, the proposed algorithm reaches an almost %100 success ratio for solving all test cases without constraint violation of the regarded problem. After the validation experiment, the proposed algorithm is applied to smart medical emergency handling in Egypt's New Galala mountainous city. Both experimental and statistical results ensure the prosperity of the proposed algorithm. Also, it ensures that MOMPA can efficiently find the shortest path to the emergency location without any collisions. [ABSTRACT FROM AUTHOR]

This paper proposes a new naturally inspired swarm intelligence algorithm called the Aptenodytes Forsteri Optimization Algorithm (AFO). The main inspiration is the emperor penguin's warm-hugging behaviour. When looking for a suitable location, emperor penguins need to sense the change of temperature, consider the location of other penguins, move closer to the centre of the penguin population, minimize their energy loss, and refer to their memory. These five strategies are evolved into five update modes of variables. According to the characteristics of these update modes in the exploration and exploitation stage, adaptive adjustment methods are designed to mix the five ways. The effectiveness of the proposed AFO is checked, through a comparison with other nature-inspired algorithms, on shifted classical benchmark problems and CEC2017 benchmark problems. Moreover, four engineering problems are utilized to estimate the effectiveness of AFO in optimizing constrained problems. The experimental results show that AFO has the best performance in most problems, which can be considered an excellent and competitive algorithm. In addition, as for the problem of partial algorithm update bias towards the origin, particular experiments and metrics are designed to examine the impact of such update methods on the generality of the algorithm. Source codes of AFO are publicly available at https://github.com/TwilightArchonYz/A-new-Nature-inspired-optimization-algorithm-AFO. [ABSTRACT FROM AUTHOR]

This paper presents an all-inclusive review of the optimization methods and their applications in fog computing. We put forward a taxonomy of the optimization techniques and then discuss their applications to solving various optimization problems in this field. Moreover, we develop a glossary of different optimization metrics in the literature and classify various evaluation environments used to test the solutions of different algorithms. The distribution of the relevant publications and the threats to the validity of the study are also discussed. Finally, we present the challenges in the existing literature and the future trends for research in fog computing optimization. [ABSTRACT FROM AUTHOR]

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as an efficient and robust optimization algorithm. FA is an algorithm that has obtained good to best results in complex problems. Therefore, there are many instances of modification, and hybridization of FA with other optimizing algorithms, but further improvements are still possible. This research first proposes a new modification of FA by introducing a novel and unique stepping ahead parameter. The concept is based on being proactive rather than reactive, which is the normal behavior of a standard FA. The notion behind stepping ahead is to send a firefly ahead then the best known position to look for even better solution. Second, a new design of a hybrid of the newly modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation further while maintaining good exploration in the fireflies is presented. The main use of CMAES in this hybrid algorithm is to provide diversity to fireflies, as a result it improves exploitation. Traditionally, hybridization has combined two or more algorithms in terms of structure only, and consideration was not given to the increase in time complexity or diversity. In this paper, the two algorithms are not run in separate cycles rather CMAES is placed inside the FA. Through this novelty, CMAES is initiated inside FA loop and an extra loop is avoided and at the same time CMAES diversifies FA solutions. The structure of algorithm together with the strength of individual solution are used. The newly established modified FA and hybrid are used to solve selected sixty five global optimization benchmark problems together with eight real-world problems from CEC 2011. The proposed algorithms have outperformed FA algorithm in both benchmark and real-world problems. The optimal solutions found by FA in benchmark problems was 69.2% while FA-Step algorithm achieved 73.9% and FA-CMAES algorithm obtained 92.3%. In real-world problem, FA obtained 37.5%, FA-Step was 50% while FA-CMAES was 75%. The results invariably show that the proposed algorithms perform significantly better than the standalone methods as well as the algorithms from the literature. • This research introduces a new and novel stepping ahead Firefly Algorithm (FA-Step). • Another proposed method is hybrid of Stepping Ahead FA with CMAES (FA-CMAES). • Proposed methods solve 65 global optimization problems and 8 real-world problems. • FA-Step and FA-CMAES achieve 73.9% and 92.3% best results in benchmark problems. • In real-world problems, FA-CMAES is able to find 7 out of 8 optimal results. [ABSTRACT FROM AUTHOR]

Differential evolution is an effective and practical approach that is widely applied for solving global optimization problems. Nevertheless, its effectiveness and scalability are decreased when the problems' dimension is increased. Hence, this paper is devoted to proposing a novel DE algorithm named quantum-based avian navigation optimizer algorithm (QANA) inspired by the extraordinary precision navigation of migratory birds during long-distance aerial paths. In the QANA, the population is distributed by partitioning into multi flocks to explore the search space effectively using proposed self-adaptive quantum orientation and quantum-based navigation consisted of two mutation strategies, DE/quantum/I and DE/quantum/II. Except for the first iteration, each flock is assigned using an introduced success-based population distribution (SPD) policy to one of the quantum mutation strategies. Meanwhile, the information flow is shared through the population using a new communication topology named V-echelon. Furthermore, we introduce two long-term and short-term memories to provide meaningful knowledge for partial landscape analysis and a qubit-crossover operator to generate the next search agents. The effectiveness and scalability of the proposed QANA were extensively evaluated using benchmark functions CEC 2018 and CEC 2013 as LSGO problems. The results were statistically analyzed by the Wilcoxon signed-rank sum test, ANOVA, and mean absolute error tests. Finally, the applicability of the QANA to solve real-world problems was evaluated by four engineering problems. The experimental results and statistical analysis prove that the QANA is superior to the competitor DE and swarm intelligence algorithms in test functions CEC 2018 and CEC 2013, with overall effectiveness of 80.46% and 73.33%, respectively. • Proposing an optimizer algorithm (QANA) inspired by migratory birds' navigation behaviors. • Introducing LTM and STM memories to share information and partial landscape analysis. • Introducing a V-echelon topology to spread information flow throughout search agents. • Introducing a quantum mutation strategy equipped by two meaningful search strategies. • The proposed QANA optimizer is very competitive and superior to compared algorithms. [ABSTRACT FROM AUTHOR]