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Quality metric-based fitness function for robust watermarking optimisation with bees algorithm

IET IMAGE PROCESSING 2016年第3期10卷 247-252页

作者： Abdelhakim, Assem M. Saleh, Hassan I. Nassar, Amin M. Egyptian Atom Energy Author Radiat Engn Dept 3 Ahmed El Zomor Cairo Egypt Cairo Univ Elect & Commun Dept Giza Egypt

The design of a robust watermarking technique has been always suffering from the conflict between the watermark robustness and the quality of the watermarked image. In this study, the embedding strength parameters for per-block image watermarking in the discrete cosine transform (DCT) domain are optimised. A fitness function is proposed to best suit the optimisation problem. The optimum solution is selected based on the quality and the robustness achieved using that solution. For a given image block, the peak-signal-to-noise ratio (PSNR) is used as a quality metric to measure the imperceptibility for the watermarked block. However, the robustness cannot be measured for a single watermark bit using traditional metrics. The proposed method uses the PSNR quality metric to indicate the degree of robustness. Hence, optimum embedding in terms of quality and robustness can be achieved. To demonstrate the effectiveness of the proposed approach, a recent watermarking technique is modified, and then used as the embedding method to be optimised. The bees algorithm is selected as the optimisation method and the proposed fitness function is applied. Experimental results show that the proposed method provides enhanced imperceptibility and robustness under different attacks.

关键词： image watermarking optimisation discrete cosine transforms embedding strength parameters per-block image watermarking DCT domain fitness function optimisation problem peak-signal-to-noise ratio PSNR quality metric bees algorithm attacks discrete cosine transforms

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Using bees algorithm for material handling equipment planning in manufacturing systems

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第9-12期48卷 1009-1018页

作者： Sayarshad, Hamid Reza Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar *** Iran

In this paper, bees algorithm (BA) has been used for determine the optimal number of material handling equipment (MHE) used on the production centers. The unmet demands become zero at the end of the planning horizon, i.e., the part demands are totally satisfied through the horizon. The newly developed model provides network information, such as unmet demands and number of loaded and empty of MHE at any given time and centers. Consequently, the model provides a tool for helping managers with planning and decision-making in manufacturing systems. Computational tests showed that small-sized instances can be solved by the exact approach in a fair amount of central processing unit time, but it is not feasible for medium and large-sized instances. To tackle this problem, a bees algorithm is proposed to solve the model. The algorithm is a search procedure inspired by the way honeybees forage for food. The results obtained show the robust performance of the bees algorithm.

关键词： bees algorithm Manufacturing systems Material handling equipment

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Training ANFIS Using the Enhanced bees algorithm and Least Squares Estimation

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2017年第2期23卷 227-234页

作者： Marzi, Hosein Darwish, Ahmed Haj Helfawi, Humam St Francis Xavier Univ Dept Informat Syst Antigonish NS Canada Univ Aleppo Fac Informat Engn Dept Artificial Intelligence & Nat Languages Aleppo Syria Univ Aleppo Fac Informat Engn Aleppo Syria

This paper presents the result of research in developing a novel training model for Adaptive Neuro-Fuzzy Inference Systems (ANFIS). ANFIS integrates the learning ability of Artificial Neural Networks with the Takagi-Sugeno Fuzzy Inference System to approximate nonlinear functions. Therefore, it is considered as a Universal Estimator. The original algorithm used in ANFIS training process has a hybrid model that uses Steepest Decent Derivative;therefore, it inherits low convergence rate and local minima during training. In this study, a training algorithm is proposed that combines bees algorithm (BA) and Least Square Estimation (LSE) (BA-LSE). The local and global exploration of BA as integrates with the best-fit solution of the LSE improves current shortcomings of ANFIS training process. The proposed training algorithm is examined under three different scenarios of function approximation, time series prediction, and classification experiments in order to verify the promising improvements in the training process of ANFIS. The experimental results validate high generalization capabilities of the BA-LSE training algorithm in comparison to the original hybrid training model of ANFIS. The new training model also enhances local minima avoidance and has high convergence rate.

关键词： bees algorithm ANFIS Fuzzy Systems Hybrid Learning

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A Permutation-Based bees algorithm for Solving Resource-Constrained Project Scheduling Problem

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019年第4期10卷 1-24页

作者： Nemmich, Mohamed Amine Fatima, Debbat Slimane, Mohamed Univ Mustapha Stambouli Mascara Dept Comp Sci Mascara Algeria Univ Tours LIFAT Tours France

In this article, a novel Permutation-based bees algorithm (PBA) is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The PBA is a modification of existing bees algorithm (BA) adapted for solving combinatorial optimization problems by changing some of the algorithm's core concepts. The algorithm treats the solutions of RCPSP as bee swarms and employs the activity-list representation and moves operators for the bees, in association with the serial scheduling generation scheme (Serial SGS), to execute the intelligent updating process of the swarms to search for better solutions. The performance of the proposed approach is analysed across various problem complexities associated with J30, J60 and J120 full instance sets of PSPLIB and compared with other approaches from the literature. Simulation results demonstrate that the proposed PBA provides an effective and efficient approach for solving RCPSP.

关键词： Activity-List Representation bees algorithm Makespan Minimisation Optimization Permutation-Based bees algorithm Project Scheduling Resource Constrained Serial Scheduling Generation Scheme

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Design of a Two-dimensional Recursive Filter Using the bees algorithm

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International Journal of Automation and computing 2010年第3期7卷 399-402页

作者： D.T.Pham Ebubekir Koc Manufacturing Engineering Centre Cardiff University Cardiff CF24 3AA UK

This paper presents the first application of the bees algorithm to the optimisation of parameters of a two-dimensional （2D） recursive digital filter. The algorithm employs a search technique inspired by the foraging behaviour of honey bees. The results obtained show clear improvement compared to those produced by the widely adopted genetic algorithm （GA）.

关键词： bees algorithm swarm intelligence optimisation two-dimensional digital filter design.

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Optimisation of swing-up control parameters for a robot gymnast using the bees algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2015年第5期26卷 1039-1047页

作者： Eldukhri, E. E. Kamil, H. G. Cardiff Univ Sch Engn Inst Mech & Mfg Engn Cardiff CF24 3AA S Glam Wales

This paper focuses on using the bees algorithm to optimise the parameters of a swing-up control for a robot gymnast (Robogymnast) attached to a freely rotating high bar mounted on ball bearings. Robogymnast, which mimics the basic movements of a human acrobat swinging on a high bar, consists of three links and three joints. Its motion is manipulated by two DC motors mounted at the shoulders and hip joints. The freely rotating high bar represents the third joint to which link 1 (hands and arms as a single rigid part) is firmly attached. Although, this triple pendulum-like structure is difficult to balance at upright posture, its unpowered joint is advantageous during the swing-up phase. The ultimate challenge was to smoothly swing up Robogymnast from the downward (stable) position to the upright (unstable) configuration by finding optimum values of the parameters that regulate the amplitudes and frequencies of the sinusoidal signals applied to the two DC motors. The bees algorithm, a novel swarm-based optimization technique, was used as to achieve this. The simulation and experimental results showed successful swing-up of Robogymnast.

关键词： Robot gymnast Swing-up control Swarm based optimization bees algorithm

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Solving fuzzy multiple objective generalized assignment problems directly via bees algorithm and fuzzy ranking

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第3期40卷 892-898页

作者： Tapkan, Pinar Ozbakir, Lale Baykasoglu, Adil Erciyes Univ Dept Ind Engn Fac Engn Kayseri Turkey Dokuz Eylul Univ Dept Ind Engn Fac Engn Izmir Turkey

In this paper, a direct solution approach for solving fuzzy multiple objective generalized assignment problems is proposed. In the problem, the coefficients and right hand side values of the constraints and the objective function coefficients are defined as fuzzy numbers. The addressed problem also has a multiple objective structure where the goals are determined so as to minimize the total cost and the imbalance between the workload of the agents. The direct solution approach utilizes the fuzzy ranking methods to rank the objective function values and to determine the feasibility of the constraints within a metaheuristic search algorithm, known as bees algorithm. Different fuzzy ranking methods, namely signed distance, integral value and area based approach are used in bees algorithm. For the computational study, the effects of these fuzzy ranking methods on the quality of the solutions are also analyzed. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Fuzzy generalized assignment problem bees algorithm Fuzzy ranking Compromise programming

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Integrating the artificial bee colony and bees algorithm to face constrained optimization problems

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INFORMATION SCIENCES 2014年 258卷 80-93页

作者： Tsai, Hsing-Chih Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei Taiwan Natl Taiwan Univ Sci & Technol Ecol & Hazard Mitigat Engn Researching Ctr Taipei Taiwan

Swarm intelligence (SI) has generated growing interest in recent decades as an algorithm replicating biological and other natural systems. Several SI algorithms have been developed that replicate the behavior of honeybees. This study integrates two of these, the artificial bee colony (ABC) and bees algorithms (BA), into a hybrid ABC-BA algorithm. In ABC-BA, an agent can perform as an ABC agent in the ABC sub-swarm and/or a BA agent in the BA sub-swarm. Therefore, the ABC and BA formulations coexist within ABC-BA. Moreover, the population sizes of the ABC and BA sub-swarms vary stochastically based on the current best fitness values obtained by the sub-swarms. This paper conducts experiments on six constrained optimization problems (COPs) with equality or inequality constraints. In addressing equality constraints, this paper proposes using these constraints to determine function variables rather than directly converting them into inequality constraints, an approach that perfectly satisfies the equality constraints. Experimental results demonstrate that the performance of the ABC-BA approximates or exceeds the winner of either ABC or BA. Therefore, the ABC-BA is recommended as an alternative to ABC and BA for handling COPs. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Constrained optimization problem Swarm intelligence Artificial bee colony bees algorithm

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Thermodynamic and Economic Optimization of Plate Fin Heat Exchangers Using the bees algorithm

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HEAT TRANSFER-ASIAN RESEARCH 2014年第5期43卷 427-446页

作者： Banooni, Salem Zarea, Hossein Molana, Maysam Shahid Chamran Univ Engn Fac Dept Mech Engn Ahwaz Iran

This study presents the successful application of the bees algorithm (BA) for optimal design of a cross- flow plate fin heat exchanger by offset strip fins. The epsilon - NTU method is used to approximate the heat exchanger effectiveness and pressure drop. Two different objective functions including the minimization of total annual cost (sum of investment and operational costs) and total number of entropy generation units for certain heat duty required under given space constraints are considered as targets of optimization separately. Based on the applications, seven design parameters (heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin-strip length, and number of hot side layers) are selected as optimization variables. Two examples from the literature are presented to illustrate the efficiency and accuracy of the proposed algorithm. Results showed that the BA can detect an optimum configuration with higher speed (short computational time) and accuracy compared to the imperialist competitive algorithm (ICA) and the genetic algorithm (GA). (C) 2013 Wiley Periodicals, Inc.

关键词： plate fin heat exchanger optimization bees algorithm imperialist competitive algorithm genetic algorithm

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A New Approach Based on Honeybee to Improve Intrusion Detection System Using Neural Network and bees algorithm

A New Approach Based on Honeybee to Improve Intrusion Detect...

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2nd International Conference on Software Engineering and Computing Systems (ICSECS 2011)

作者： Ali, Ghassan Ahmed Jantan, Aman Univ Sains Malaysia George Town Malaysia

ISBN: (纸本)9783642222023

A new approach inspired by bees defensive behaviour in nature is proposed to improve Intrusion Detection System (IDS). In honeybee colonies, guards discriminate nestmates from non-nestmates at a hive entrance using an approach contains Undesirable-Absent (UA) or Desirable-Present (DP), and Filtering Decision (FD) methods. These methods are used to detect intruder and classify its type. In the proposed approach, the UA detector is responsible for detecting pre-defined attacks based on their attack signatures. Neural network trained by bees algorithm (BA) was used to learn the patterns of attacks given in training dataset and use these patterns to find specific attacks in test dataset. The DP detector is responsible for detecting anomalous behaviours based on the trained normal behaviour model. Finally, FD method is used to train the UA detector in real-time to detect new intrusions. The performance of the proposed IDS is evaluated by using KDD'99 dataset, the benchmark dataset used by IDS researchers. The experiments show that the proposed approach is applied successfully and able to detect many different types of intrusions, while maintaining a low false positive rate.

关键词： Intrusion detection system honeybee approach neural networks bees algorithm

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