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The Simplified Aquila Optimization algorithm

IEEE ACCESS 2022年 10卷 22487-22515页

作者： Zhao, Juan Gao, Zheng-Ming Chen, Hua-Feng Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Hubei Jingmen Ind Technol Res Inst Inst Intelligent Informat Technol Jingmen 448000 Peoples R China

Considering the complicated multimodality, separability, scalability, symmetry characteristics of real-world engineering problems, we are still under the way to find more capable algorithms to solve them. The Aquila optimization (AO) algorithm was just proposed recently, however, the original version of this algorithm had some defects in the exploitation procedure, and resulted in incapability to exploit more information. Therefore, a simplified improved AO (IAO) algorithm was proposed and the equation controlling the exploitation procedure was removed, the former two strategies were kept and the latter two were dropped. To confirm the capability of the proposed IAO, simulation experiments on unimodal, multimodal benchmark functions and the CEC21 basic functions were carried out. Simulation experiments on three famous real-world engineering problems were also carried out. Comparison were made with the original AO algorithm, and some other newly proposed nature-inspired algorithms. Results on benchmark functions and the CEC14 basic functions verified the better performance, however, results on the real-world engineering problems demonstrated an incapable conclusion. Detailed future work is needed to tell the reason.

关键词： Optimization Mathematical models Benchmark testing Statistics Sociology Convergence Licenses Aquila optimization algorithm meta-heuristic algorithm nature-inspired algorithm benchmark function engineering problems

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Three-dimensional route planning for multiple unmanned aerial vehicles using Salp Swarm algorithm

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2023年第7期35卷 1059-1078页

作者： Dewangan, Ram Kishan Saxena, Priyansh Thapar Inst Engn & Technol ECED Patiala Punjab India ABV Indian Inst Informat Technol & Management ABV IIITM IT Gwalior India

Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of translation and rotational steps from a given start location to the destination goal location. The goal of the route planning problem is to determine the most optimal route avoiding any collisions with the obstacles present in the environment. Route planning is an NP-hard optimisation problem. In this paper, a newly proposed Salp Swarm algorithm (SSA) is used, and its performance is compared with deterministic and other nature-inspired algorithms (NIAs). The results illustrate that SSA outperforms all the other meta-heuristic algorithms in route planning for multiple UAVs in a 3D environment. The proposed approach improves the average cost and overall time by 1.25% and 6.035%, respectively, when compared to recently reported data. Route planning is involved in many real-life applications like robot navigation, self-driving car, autonomous UAV for search and rescue operations in dangerous ground-zero situations, civilian surveillance, military combat and even in commercial services like package delivery by drones.

关键词： Meta-heuristic nature-inspired algorithm Route planning Salp Swarm algorithm UAV Path planning

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Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第10期10卷 2548-2561页

作者： Verma, Sumit Mukherjee, Vivekananda Indian Sch Mines Dept Elect Engn Dhanbad Jharkhand India

Considering system uncertainties prevailing in the restructured power system, congestion management (CM) is a vital issue in power system operation and control. The objective of CM is to remove congestion in the lines while satisfying all the constraints with minimum congestion cost. This study proposes a generation rescheduling-based approach for CM in electricity market using a novel ant lion optimiser (ALO) algorithm. ALO is a recently developed the nature-inspired algorithm based on the hunting mechanism of antlions. The effectiveness of the proposed approach is tested on modified IEEE 30-bus, modified IEEE 57-bus and IEEE 118-bus test systems. The security constraints like load bus voltage and line loading impact are incorporated in this study. To prove the validity of the proposed technique, the obtained results are compared to the results offered by various recent optimisation algorithms. The results show that the proposed ALO algorithm outperforms the other comparative algorithms. The proposed approach uses less number of fitness function evaluations, not traps into local minima and offers promising convergence characteristic. The proposed approach will ease the system operator to remove the contingency rapidly for secured and reliable operation of the power system under deregulated environment.

关键词： optimisation power generation scheduling power system management power system security generator optimal real power rescheduling congestion management ant lion optimiser generation rescheduling-based approach electricity market ALO algorithm nature-inspired algorithm antlion hunting mechanism security constraints load bus voltage line loading deregulated environment

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A comprehensive review of firefly algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2013年第0期13卷 34-46页

作者： Fister, Iztok Fister, Iztok, Jr. Yang, Xin-She Brest, Janez Univ Maribor Fac Elect Engn & Comp Sci Maribor 2000 Slovenia Middlesex Univ Sch Sci & Technol London NW4 4BT England

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Firefly algorithm Swarm intelligence nature-inspired algorithm Optimization

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Using Social Behavior of Beetles to Establish a Computational Model for Operational Management

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2020年第2期7卷 492-502页

作者： Khan, Ameer Hamza Cao, Xinwei Li, Shuai Luo, Chunbo Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Shanghai Univ Sch Management Shanghai 201900 Peoples R China Swansea Univ Dept Elect & Elect Engn Swansea SA1 8EN W Glam Wales Univ Exeter Dept Comp Sci Exeter EX4 4QJ Devon England

In this article, we computationally model the social behavior of beetles and apply it to the tracking control of manipulators. The beetles demonstrate excellent skills to forage food in a previously unknown environment by merely using their olfactory senses. The goal of the beetle is to search the region with the maximum smell. Therefore, the actions of the beetle can be characterized as an optimization algorithm. This article mathematically models this behavior in the form of a recurrent neural network (RNN) with a temporal-feedback connection. We apply the formulated RNN controller for the redundancy resolution and tracking control of the redundant manipulators with an unknown kinematic model. Most of the industrial robots have redundant manipulators, and kinematic trajectory tracking is a fundamental problem for any industrial task. The behavior of the beetle allows us to formulate a position-level controller without relying on the manipulation of the Jacobian matrix. It is in contrast with the conventional velocity-level controllers, which require an accurate kinematic model of the manipulator and calculation of pseudoinverse of Jacobian, a computationally expensive task. The proposed algorithm, called Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm, is capable of driving the manipulator by only using the feedback from the position and orientation sensors. The stability and convergence of the proposed algorithm are theoretically proved, and the simulations results using a seven-degree-of-freedom (DOF) industrial robotic arm, KUKA LBR IIWA14, are presented to demonstrate the performance of the proposed algorithm.

关键词： Metaheuristic optimization model-free control nature-inspired algorithm operational management

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Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2023年第6期30卷 2945-2971页

作者： Camacho-Villalon, Christian L. Dorigo, Marco Stuetzle, Thomas Univ Libre Bruxelles Inst Rech Interdisciplinaires & Dev Intelligence B-1050 Brussels Belgium

We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well-established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self-proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor-based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no-free-lunch theorems for optimization.

关键词： nature-inspired algorithm particle swarm optimization evolution strategies novel algorithm swarm intelligence

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nature-inspired multimedia service composition in a media cloud-based healthcare environment

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2016年第4期19卷 2251-2260页

作者： Alamri, Atif King Saud Univ Dept Informat Syst CCIS Riyadh Saudi Arabia

This paper presents a nature-inspired service composition framework for mobile cloud media services, which attempts to solve the mismatch between resource-hungry rich media content and power-limited smart phones used by healthcare professionals. Our objective was to design and develop on-demand and optimal media service composition schemes (combining various individual services such as media streaming services, transcoding services, and payment services). The technique considers the users' quality of service (QoS) requirements and is self-optimizing, self-healing, self-configuring, and self-protecting on a mobile media cloud platform. Media cloud services provide similar functionality but different QoS and can dynamically join or leave the cloud. We propose an approach for choosing an optimal service composition path is to formulate an optimization problem that considers the QoS criteria, and then solve the problem using a nature-inspired technique such as the bee-based algorithm. We evaluated the suitability of this framework through simulations and a test-bed.

关键词： Automatic service composition QoS nature-inspired algorithm Media cloud computing Healthcare cloud

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Dragonfly algorithm: a comprehensive review and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第21期32卷 16625-16646页

作者： Meraihi, Yassine Ramdane-Cherif, Amar Acheli, Dalila Mahseur, Mohammed Univ MHamed Bougara Boumerdes LIST Lab Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France Univ MHamed Bougara Boumerdes LAA Lab Ave Independence Boumerdes 35000 Algeria Univ Sci & Technol Houari Boumediene Fac Elect & Informat Algiers 16025 Algeria

Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and its new variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the paper suggests some possible interesting research on the applications and hybridizations of DA for future works.

关键词： Dragonfly algorithm nature-inspired algorithm Swarm intelligence Meta-heuristics

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Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 111卷 175-198页

作者： Chen, Hao Heidari, Ali Asghar Chen, Huiling Wang, Mingjing Pan, Zhifang Gandomi, Amir H. Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work. HHO is a recently developed swarm-based stochastic algorithm that has previously shown excellent performance. In fact, the original HHO has features that can still be improved as it may experience convergence problems or may easily become trapped in local optima. To overcome these shortcomings of the original HHO, the first powerful variant of HHO integrates chaos strategy, topological multi-population strategy, and differential evolution (DE) strategy. For this, chaos mechanism is first introduced into the original algorithm to improve the exploitation propensities of HHO. The multi-population strategy with three mechanisms is embedded to augment the global search ability of the method. Finally, the DE mechanism is introduced into the HHO to enhance the quality of the solutions. Based on these well-regarded evolutionary mechanisms, we propose an enhanced DE-driven multi-population HHO (CMDHHO) algorithm. In this work, the proposed CMDHHO is compared with a range of other methods, including four original meta-heuristic algorithms, conventional HHO, twelve advanced algorithms based on IEEE CEC2017 benchmark functions, and IEEE CEC2011 real-world problems. Furthermore, the Friedman test and the non-parametric statistical Wilcoxon sign rank test are used to verify the significance of the results. The results of the experiments show that the three embedded mechanisms can effectively enhance the exploratory and exploitative traits of HHO. The time required for HHO to converge was substantially shortened. We suggest using the proposed CMDHHO as an effective tool to solve complex optimization problems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Harris hawks optimization Optimization nature-inspired algorithm Differential evolution Metaheuristic

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Evaluation of Advanced Jaya algorithm in engineering design problems: a comparative study with recent metaheuristics

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2024年第4期49卷 1-13页

作者： Albayrak, Gulcag Albayrak, Ugur Eskisehir Osmangazi Univ Fac Engn & Architecture Dept Civil Engn TR-26040 Eskisehir Turkiye

One of the most recent algorithms called Advanced Jaya algorithm (A-JA) is a combination of evolutionary technique, swarm-based metaheuristic and population-based approach. In this study, A-JA is investigated as an optimization method for engineering problems found in the literature. To assess the capability of A-JA, ten design problems that have different mathematical models were employed. In addition, the same problems were solved using popular algorithms such as Particle Swarm Optimization (PSO) and Genetic algorithm (GA), as well as less familiar metaheuristics like Harris Hawk Optimization (HHO) and Grey Wolf Optimization (GWO). The results showed that A-JA produces efficient outcomes, such as the ability to establish an effective equilibrium between exploration and exploitation and to escape from local optimums. Thus, the results indicate that A-JA often reports better solutions than other optimization algorithms.

关键词： Advanced Jaya algorithm optimization engineering design problems nature-inspired algorithm

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