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A new meta-heuristic ebb-tide-fish-inspired algorithm for traffic navigation

TELECOMMUNICATION SYSTEMS 2016年第2期62卷 403-415页

作者： Meng, Zhenyu Pan, Jeng-Shyang Alelaiwi, Abdulhameed Shenzhen Grad Sch Harbin Inst Technol Dept Comp Sci Innovat Informat Ind Res Ctr Shenzhen Peoples R China Fujian Univ Technol Coll Informat Sci & Engn Fuzhou Peoples R China King Saud Univ Coll Comp & Informat Sci Dept Software Engn POB 51178 Riyadh 11543 Saudi Arabia

More and more bio-inspired or meta-heuristic algorithms have been proposed to tackle the tough optimization problems. They all aim for tolerable velocity of convergence, a better precision, robustness, and performance. In this paper, we proposed a new algorithm, ebb tide fish algorithm (ETFA), which mainly focus on using simple but useful update scheme to evolve different solutions to achieve the global optima in the related tough optimization problem rather than PSO-like velocity parameter to achieve diversity at the expenses of slow convergence rate. The proposed ETFA achieves intensification and diversification in a new way. First, a flag is used to demonstrate the search status of each particle candidate. Second, the single search mode and population search mode tackle the intensification and diversification for tough optimization problem respectively. We also compare the proposed algorithm with other existing algorithms, including bat algorithm, cat swarm optimization, harmony search algorithm and particle swarm optimization. Simulation results demonstrate that the proposed ebb tide fish algorithm not only obtains a better precision but also gets a better convergence rate. Finally, the proposed algorithm is used in the application of vehicle route optimization in Intelligent Transportation Systems (ITS). Experiment results show that the proposed scheme also can be well performed for vehicle navigation with a better performance of the reduction of gasoline consumption than the shortest path algorithm (Dijkstra algorithm) and A* algorithm.

关键词： Benchmark function bio-inspired algorithm Ebb tide fish algorithm Gasoline consumption Metaheuristic optimization Vehicle navigation

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Binary bat algorithm

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NEURAL COMPUTING & APPLICATIONS 2014年第3-4期25卷 663-681页

作者： Mirjalili, Seyedali Mirjalili, Seyed Mohammad Yang, Xin-She Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Shahid Beheshti Univ Fac Elect & Comp Engn Dept Elect Engn Tehran *** Iran Univ Cambridge Dept Engn Cambridge CB2 1PZ England

Bat algorithm (BA) is one of the recently proposed heuristic algorithms imitating the echolocation behavior of bats to perform global optimization. The superior performance of this algorithm has been proven among the other most well-known algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO). However, the original version of this algorithm is suitable for continuous problems, so it cannot be applied to binary problems directly. In this paper, a binary version of this algorithm is proposed. A comparative study with binary PSO and GA over twenty-two benchmark functions is conducted to draw a conclusion. Furthermore, Wilcoxon's rank-sum nonparametric statistical test was carried out at 5 % significance level to judge whether the results of the proposed algorithm differ from those of the other algorithms in a statistically significant way. The results prove that the proposed binary bat algorithm (BBA) is able to significantly outperform others on majority of the benchmark functions. In addition, there is a real application of the proposed method in optical engineering called optical buffer design at the end of the paper. The results of the real application also evidence the superior performance of BBA in practice.

关键词： Bat algorithm Discrete evolutionary algorithms Binary optimization Optical buffer design Optimization Discrete optimization bio-inspired algorithm

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bio-inspired Proximity Discovery and Synchronization for D2D Communications

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IEEE COMMUNICATIONS LETTERS 2013年第12期17卷 2300-2303页

作者： Chao, Shih-Lung Lee, Hsin-Ying Chou, Ching-Chun Wei, Hung-Yu Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan

In this paper, a distributed mechanism for application-aware proximity services (ProSe) in device to device (D2D) communications is presented. The method, which is derived from the bio-inspired firefly algorithm, can achieve proximity discovery and synchronization at one time. To be precise, this mechanism not only enables neighbour discovery and service discovery simultaneously, but achieves synchronization in physical communication timing and service interests in the meanwhile. However, the basic firefly algorithm is not perfectly suitable for larger scale systems such as LTE-A D2D. In most network topologies, the property that each node may not be able to hear all its neighbours makes the basic version fail. Thus, we further propose the firefly spanning tree (FST) algorithm which can solve the topology-dependent divergence problems.

关键词： D2D bio-inspired algorithm application-aware services proximity discovery synchronization

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Modified Adaptive Cuckoo Search (MACS) algorithm and formal description for global optimisation

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第2期44卷 73-79页

作者： Zhang, Yongwei Wang, Lei Wu, Qidi Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

bio-inspired algorithms, through imitating the regular pattern of life forms, often produce unexpected results. A novel global optimisation algorithm, Cuckoo Search (CS), is an example that simulates the brood behaviour of some species of cuckoos. By using Levy distribution, the flying pattern of cuckoos is also imitated. However, the potential of cuckoo's search pattern is not fully discovered in CS algorithm. In this article, we introduce the CS algorithm and associated Levy flights. A Modified Adaptive Cuckoo Search (MACS) is then proposed by introducing grouping, parallel, incentive, adaptive and information-sharing characteristics. Also, the formal descriptions of improving strategies are given. The proposed algorithm improves the basic CS algorithm without losing the characteristic of high-efficiency search of Levy flights. Experiment results show that MACS outperforms basic CS algorithm on most test problems and possesses application potential for real-world problems.

关键词： cuckoo search global optimisation Levy flights swarm intelligence foraging strategy population diversity bio-inspired algorithm formal description

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A critical discussion into the core of swarm intelligence algorithms

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EVOLUTIONARY INTELLIGENCE 2019年第2期12卷 189-200页

作者： Ferreira Cruz, Davila Patricia Maia, Renato Dourado De Castro, Leandro Nunes Univ Prebiteriana Mackenzie Grad Program Elect Engn & Comp Sao Paulo Brazil Univ Estadual Montes Claros Dept Comp Sci Montes Claros MG Brazil

The literature is now filled with swarm intelligence algorithms developed by taking inspiration from a number of insects and other animals and phenomena, such as ants, termites, bees, fishes and cockroaches, to name just a few. Many, if not most, of these bioinspirations carry with them some common issues and features which happen at the individual level, promoting very similar collective emergent phenomena. Thus, despite using different biological metaphors as inspiration, most algorithms present a similar structure and it is possible to identify common macro-processes among them. In this context, this paper identifies a set of common features among some well-known swarm-based algorithms and how each of these approaches implement them. By doing this, we provide the community with the core features of swarm-intelligence algorithms. This diagnostic is crucial and timely to the field, because once we are able to list and explain these commonalities, we are also able to better analyze and design swarm intelligence algorithms.

关键词： Swarm intelligence bio-inspired algorithm Social insect Meta-heuristic

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Real-time,decentralized and bio-inspired topology control for holonomic autonomous vehicles

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International Journal of Intelligent Computing and Cybernetics 2012年第3期5卷 359-380页

作者： Cem Safak Sahin M.Ümit Uyar Advanced Information Technologies BAE SystemsBurlingtonMassachusettsUSA Department of Electrical Engineering The City College of The City University of New YorkNew YorkUSA

Purpose–This paper aims to present an approach for a bio-inspired decentralization topology control mechanism,called force-based genetic algorithm(FGA),where a genetic algorithm(GA)is run by each holonomic autonomous vehicle(HAV)in a mobile ad hoc network(MANET)as software agent to achieve a uniform spread of HAVs and to provide a fully connected network over an unknown geographical *** HAV runs its own FGA to decide its next movement direction and speed based on local neighborhood information,such as obstacles and the number of neighbors,without a centralized control unit or global ***/methodology/approach-The objective function used in FGA is inspired by the equilibrium of the molecules in physics where each molecule tries to be in the balanced position to spend minimum energy to maintain its *** this approach,a virtual force is assumed to be applied by the neighboring HAVs to a given *** equilibrium,the aggregate virtual force applied to an HAV by its neighbors should sum up to *** the aggregate virtual force is not zero,it is used as a fitness value for the *** value of this virtual force depends on the number of neighbors within the communication range of Rcom and the distance among *** chromosome in our GA-based framework is composed of speed and movement *** FGA is independently run by each HAV as a topology control mechanism and only utilizes information from neighbors and local terrain to make movement and speed decisions to converge towards a uniform distribution of *** authors developed an analytical model,simulation software and several testbeds to study the convergence properties of the ***-The paper finds that coverage-centric,bio-inspired,mobile node deployment algorithm ensures effective sensing coverage for each mobile node after initial *** FGA is also an energy-aware self-organization framework since it reduces energy consumption by eliminating unnecessary excessive mo

关键词： Genetic algorithms Spatial node distribution Mobile ad hoc networks Topology control bio-inspired algorithm Analytical model Mobile networks

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Detection of white blood cells using optimized qGWO

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2021年第1期15卷 141-149页

作者： Sharma, Prerna Sharma, Moolchand Gupta, Divij Mittal, Nimisha GGSIPU Maharaja Agrasen Inst Technol New Delhi India

This paper presents an optimized quantum Grey Wolf Optimization algorithm (qGWO), which is an enhanced version of the Grey Wolf optimization algorithm for feature selection of blood cells, which can further used for the detection of WBCs. White blood cells count in the human body determines the immune system of the human body. A deviation in the count of WBCs from the normal cell count in the human body may indicate an abnormal condition. The proposed model uses a quantum grey wolf optimization algorithm for the detection of White Blood cells among the dataset of various types of blood cells. Quantum Grey Wolf algorithm is used to find the minimal set of optimal features from the set of available features to detect the White Blood Cells optimally. As the ordinary Grey Wolf Optimization algorithm also used to find the minimal set of optimal features, but the features selected by qGWO are better in terms of computational time. Further, several classification algorithms such as Support Vector Machine (SVM), Random Forest algorithm, K Nearest Neighbor(KNN) algorithm are applied to the model to predict its accuracy for the selected subset of features after feature selection. The performance of several classifiers is compared, and the model attained the maximum accuracy of 97.8% using KNN with minimum computational time. The result obtained shows that the algorithm proposed is capable of finding an optimal subset of features and maximizing the accuracy.

关键词： White blood cells quantum grey wolf optimization (qGWO) feature extraction optimization bio-inspired algorithm

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An Osmosis-Based Intelligent Agent Scheduling Framework for Cloud Bursting in a Hybrid Cloud

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INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2020年第3期11卷 68-88页

作者： Hepsiba, Preethi Sheba Kanaga, Grace Mary E. CMR Inst Technol Comp Sci & Engn Dept Bengaluru India Karunya Inst Technol & Sci Coimbatore Tamil Nadu India

An intelligent system to efficiently provision resources in a hybrid cloud environment is necessary due to the high level of complexity. The semi-permeable agent for hybrid cloud scheduling (SPAH) is a bio-inspired agent that adapts the biological process of osmosis into cloud bursting. The primary objective of the agent is to minimize the makespan. The framework and algorithm for the two phases of SPAH, to recognize the state and decide on action are presented. A QoS (Quality of Service) deadline factor metric is proposed to study the indirect impact of SPAH in deadline satisfaction. SPAH shows significant improvement in deadline satisfaction of up to 85% as compared to other cloud bursting techniques. This is the result of a reduced makespan and a reduced cumulative waiting time. The analysis of SPAH shows that it works in quadratic time complexity.

关键词： bio-inspired algorithm Cloud Bursting Cloud Computing Distributed Computing Intelligent Agent Quality Of Service (Qos) Resource Provisioning Scheduling

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Decentralized underwater multi-robot communication using bio-inspired approaches

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ARTIFICIAL LIFE AND ROBOTICS 2015年第2期20卷 152-158页

作者： Sutantyo, Donny Levi, Paul Univ Stuttgart Inst Parallel & Distributed Syst D-70174 Stuttgart Germany

Interference and jamming become crucial issues in swarm robot communication and sensing, especially in underwater applications. In this paper, an algorithm for facilitating scheduling within robotic swarm is formulated and its investigation in a series of experiments is described. The scheduling algorithm is biologically inspired by the firefly and frog. Novel distributed models for both algorithms are also investigated to improve scalability and provide decentralization of the algorithms. It is proven from the simulated experiments that the model is robust and viable for scheduling underwater swarm communication and sensing. Moreover, the paper illustrates that global swarm cognition can also be achieved using the algorithm to estimate the swarm density in a decentralized way. Finally, a real robot experiment using underwater swarm robot platforms is also presented.

关键词： Underwater robotics Swarm robotics Swarm communication Coupled oscillator bio-inspired algorithm

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Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

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SCIENTIFIC REPORTS 2024年第1期14卷 1-20页

作者： Huang, Zhendai Zhang, Zhen Hua, Cheng Liao, Bolin Li, Shuai Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Changsha Univ Sch Elect Informat & Elect Engn Changsha 410003 Peoples R China Univ Oulu Fac Informat Technol & Elect Engn Oulu 90570 Finland VTT Tech Res Ctr Finland Kaitovayla 1 Oulu 90570 Finland

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users' expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio's efficient frontier.

关键词： Large language models Prompt engineering Egret swarm optimal algorithm Portfolio selection bio-inspired algorithm

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