Hanning Chen

A hybrid artificial bee colony optimizer by combining with life-cycle, Powell's search and crossover

This paper proposes a hybrid artificial bee colony optimizer (HABC) by restructuring the artificial bee colony system with life-cycle, Powells search and social learning. The proposed HABC based on life-cycle is a cooperative and varying-population model where the bee can switch its state periodically according to the local environmental landscape. Through this new characteristic, two significant merits of reducing redundant search and maintaining diversity of population can be obtained. In addition, with the social learning, the information exchange ability of the bees can be enhanced in the early exploration phase while the Powells method enables the bees to deeply exploit around the promising area, which provides an appropriate balance between exploration and exploitation. Then, eight basic benchmarks, seven CEC 2005 composite functions, and a real-world problem of RFID networks optimization are solved by HABC, successively. The experimental results validate the incorporated combinatorial strategies and demonstrate the performance superiority of HABC.

A novel bionic algorithm inspired by plant root foraging behaviors

A new bionic algorithm is proposed based on the incentive mechanism of plant root branching, regrowing and tropisms.A new plant root foraging strategy simulates the plant root tropism mechanism and sets up the dynamics mechanism of root growing rapidly towards the global optima.The auxin concentration is set up to determine how to select new growing points and branching number of roots.The mainroot regrowing and the lateral-roots regrowing operators can make appropriate balances between exploration and exploitation. In this contribution, a novel bionic algorithm inspired by plant root foraging behaviors, namely artificial root foraging optimization (ARFO) algorithm, is designed and developed. The incentive mechanism of ARFO is to mimic the adaptation and randomness of plant root foraging behaviors, e.g., branching, regrowing and tropisms. A mathematical architecture is firstly designed to model the plant root foraging pattern. Under this architecture, the effects of the tropism and the self-adaptive growth behaviors are investigated. Afterward, the arithmetic realization of ARFO derived from this framework is presented in detail. In order to demonstrate the optimization performance, the proposed ARFO is benchmarked against several state-of-the-art reference algorithms on a suit of CEC 2013 and CEC 2014 functions. Computational results show a high performance of the proposed ARFO for searching a global optimum on several benchmarks, which indicates that ARFO has potential to deal with complex optimization problems.

Root growth model: a novel approach to numerical function optimization and simulation of plant root system

This paper presents a general optimization model gleaned ideas from root growth behaviours in the soil. The purpose of the study is to investigate a novel biologically inspired methodology for complex system modelling and computation, particularly for optimization of higher-dimensional numerical function. For this study, a mathematical framework and architecture are designed to model root growth patterns of plant. Under this architecture, the interactions between the soil and root growth are investigated. A novel approach called “root growth algorithm” (RGA) is derived in the framework and simulation studies are undertaken to evaluate this algorithm. The simulation results show that the proposed model can reflect the root growth behaviours of plant in the soil and the numerical results also demonstrate RGA is a powerful search and optimization technique for higher-dimensional numerical function optimization.

Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.

Artificial Bee Colony Optimizer Based on Bee Life-Cycle for Stationary and Dynamic Optimization

This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

Bacterial colony foraging for multi-mode product colour planning

In this work, in order to assist designer in colour planning during product development, an efficient synthesised evaluation model is presented to evaluate colour-combination schemes of multi-working modes products MMP. A novel bacterial colony foraging BCF algorithm is proposed to search for the optimal colour-combination schemes of MMP based on the evaluation model. The proposed BCF extend original bacterial foraging algorithm to adaptive and cooperative mode by combining bacterial chemotaxis, cell-to-cell communication, and a self-adaptive foraging strategy. The experiment presents an exhaustive comparison of the proposed BCF and two successful bio-inspired search techniques, namely the genetic algorithm GA and particle swarm optimisation PSO, on three MMP tested cases of different nature, namely a hair-drier with two-coloured areas and two working modes, and two arm-type aerial work platforms both two-coloured products while with two and three working modes, respectively. Simulation results demonstrate that the proposed method is feasible and efficient.

Two-Level Master-Slave RFID Networks Planning via Hybrid Multiobjective Artificial Bee Colony Optimizer

Radio frequency identification (RFID) networks planning (RNP) is a challenging task on how to deploy RFID readers under certain constraints. Existing RNP models are usually derived from the flat and centralized-processing framework identified by vertical integration within a set of objectives which couple different types of control variables. This paper proposes a two-level RNP model based on the hierarchical decoupling principle to reduce computational complexity, in which the cost-efficient planning at the top levels is modeled with a set of discrete control variables (i.e., switch states of readers), and the quality of service objectives at the bottom level are modeled with a set of continuous control variables (i.e., physical coordinate and radiate power). The model of the objectives at the two levels is essentially a multiobjective problem. In order to optimize this model, this paper proposes a specific multiobjective artificial bee colony optimizer called H-MOABC, which is based on performance indicators with reinforcement learning and orthogonal Latin squares approach. The proposed algorithm proves to be competitive in dealing with two-objective and three-objective optimization problems in comparison with state-of-the-art algorithms. In the experiments, H-MOABC is employed to solve the two scalable real-world RNP instances in the hierarchical decoupling manner. Computational results shows that the proposed H-MOABC is very effective and efficient in RFID networks optimization.

Outlier Detection for Control Process Data Based on Improved ARHMM

In view of the difficulty of accurate online detection for massive data collecting real-timely in a strong noise environment during control process, an order self-learning Autoregressive Hidden Markov Model (ARHMM) algorithm is proposed to carry out online outlier detection in industrial control process. The algorithm utilizes AR model to fit the time series and makes use of HMM as basic detection tool, which can avoid the deficiency of presetting the threshold in traditional detection methods. In order to update parameters of ARHMM online, the structure of traditional Brockwell–Dahlhaus–Trindade (BDT) algorithm is improved to be a double-iterative structure in which iterative calculation from both time and order is applied respectively. With the purpose of reducing the influence of outlier on parameter update of ARHMM, the strategies of detection-before-update and detection-based-update are adopted, which also improve the robustness of algorithm. Subsequent simulation by model data and practical application verify the accuracy, robustness and property of online detection of the algorithm. According to the result, it is obvious that new algorithm proposed in this paper is more suitable for outlier detection of control process data in process industry.

A restructured artificial bee colony optimizer combining life-cycle, local search and crossover operations for droplet property prediction in printable electronics fabrication

For printable electronics fabrication, a major challenge is the print resolution and accuracy delivered by a drop-on-demand piezoelectric inkjet printhead. In order to meet the challenging requirements of printable electronics fabrication, this paper proposes a novel restructured artificial bee colony optimizer called HABC for optimal prediction of the droplet volume and velocity. The main idea of HABC is to develop an adaptive and cooperative scheme by combining life-cycle, Powell’s search and crossover-based social learning strategies for complex optimizations. HABC is a more biologically-realistic model that the reproduce and die dynamically throughout the foraging process and the population size varies as the algorithm runs. With the crossover operator, the information exchange ability of the bees can be enhanced in the early exploration phase while the Powell’s search enables the bees deeply exploit around the promising area, which provides an appropriate balance between exploration and exploitation. The proposed algorithm is benchmarked against other four state-of-the-art bio-inspired algorithms using both classical and CEC2005 test function suites. Then HABC is applied to predict the printing quality using nano-silver ink. Statistical analysis of all these tests highlights the significant performance improvement due to the beneficial combination and shows that the proposed HABC outperforms the reference algorithms.