Xiaodan Liang

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.

Droplet property optimization in printable electronics fabrication using root system growth algorithm

Abstract For printable electronics fabrication, the printing quality delivered by a Drop-on-Demand (DoD) Piezoelectric Inkjet (PIJ) Printhead is limited due to the residual vibration problem in the ink channel. The residual vibration after jetting the ink drop influences the droplet velocity and volume consistency, and limits the maximum jetting frequency of the printhead. In order to meet the challenging requirements of printable electronics fabrication, this work proposes a novel Optimization framework for optimal parameter setting of the high-frequency driving waveform. A novel Root System Growth Algorithm (RSGA), based on principles from plant root growth and foraging behaviors, is chosen as the core optimization algorithm of the framework. The proposed RSGA is benchmarked against other four state-of-the-art bio-inspired algorithms using CEC2005 test function suite. Then several groups of experiment results for various targets are presented to demonstrate the universality of the proposed optimization-based search system for the improvement of printing quality. Simulation results show that the RSGA has an outstanding performance in searching the driving waveform parameters for specified droplet properties.

Indicator-based multi-objective adaptive bacterial foraging algorithm for RFID network planning

This work develops a novel indicator-based multi-objective bacterial colony foraging algorithm (I-MOBCA) for complex multi-objective or many-objective optimization problems. The main idea of I-MOBCA is to develop an adaptive and cooperative model by combining bacterial foraging, adaptive searching, cell-to-cell communication and preference indicator-based measure strategies. In this algorithm, each bacterium can adopt its run-length unit to appropriately balance exploitation and exploration states, and the quality of position or solution is calculated on the basis of the binary quality indicator to determine the Pareto dominance relation. Our algorithm uses Pareto concept and preference indicator-based measure to determine the non-dominated solutions in each generation, which can essentially reduce the computation complexity. With several mathematical benchmark functions, I-MOBCA is proved to have significantly better performance over compared algorithms for solving some complex multi-objective optimization problems. Then the proposed I-MOBCA is used to solve three-objective RFID network planning problem. Simulation results show that I-MOBCA proves to be superior for planning RFID networks than compared algorithms in terms of optimization accuracy and computation robustness.

Root system growth biomimicry for global optimization models and emergent behaviors

Terrestrial plants have evolved remarkable adaptability that enables them to sense environmental stimuli and use this information as a basis for governing their growth orientation and root system development. In this paper, we explain the foraging behaviors of plant root and develop simulation models based on the principles of adaptation processes that view root growing as optimization. This provides us with new methods for global optimization. Accordingly a novel bioinspired optimizer, namely the root system growth algorithm (RSGA), is proposed, which adopts the root foraging, memory and communication and auxin-regulated mechanism of the root system. Then RSGA is benchmarked against several state-of-the-art reference algorithms on a suit of CEC2014 functions. Experimental results show that RSGA can obtain satisfactory performances on several benchmarks in terms of accuracy, robustness and convergence speed. Moreover, a comprehensive simulation is conducted to investigate the explicit adaptability of root system in RSGA. That is, in order to be able to climb noisy gradients in nutrients in soil, the foraging behaviors of root system are social and cooperative that is analogous to animal foraging behaviors.

Biomimicry of symbiotic multi-species coevolution for discrete and continuous optimization in RFID networks

In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS2Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS2O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS2O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm’s performance. Then PS2O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

Adaptive Bacterial Foraging Algorithm and Its Application in Mobile Robot Path Planning

This work considered the utilization of biomimicry of bacterial foraging strategy to develop an adaptive control strategy for mobile robot, and proposed a bacterial foraging approach for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation studies, a test scenario of static environment with different number obstacles is adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.

Biomimicry of Plant Root Foraging for Distributed Optimization: Models and Emergent Behaviors

Terrestrial plants have evolved remarkable adaptability that enables them to sense environmental stimuli and use this information as a basis for governing their growth orientation and root system development. In this paper, we explain the foraging behaviors of plant root and develop simulation models based on the principles of adaptation processes that view root growing as optimization. This provides us with novel models of plant root foraging behavior and with new methods for global optimization. This model is instantiated as a novel bio-inspired optimization model, which adopts the root foraging, memory and communication, and auxin-regulated mechanisms of the root system. We perform comprehensive simulation to demonstrate that the proposed model exhibit the property identified by natural plant root system. That is, in order to be able to climb noisy gradients in nutrients in soil, the foraging behaviors of root system is social and cooperative that is analogous to animal foraging behaviors.

A NSGA-II with ADMM Mutation for Solving Multi-objective Robust PCA Problem

Robust Principal Component Analysis is generalized to a multi-objective optimization problem, named as Multi-objective Robust Principal Component Analysis (MRPCA) in this paper. We aim to solve MRPCA via Evolutionary Algorithm. To the best knowledge of authors, this is the first attempt to use evolutionary algorithm to solve MRPCA problem, which is a high dimension convex optimization problem. Specifically, one of the popular evolutionary algorithm, NSGA-II, is tested on MRPCA problem. The curse of dimensionality is observed when the dimension of MRPCA problem increases. Since this problem is convex, which is a friendly structure, we propose a modified NSGA-II by introducing a new mutation method: ADMM (Alternating Direction Method of Multipliers) mutation. Numerical experiments show our modified NSGA-II algorithm converges much faster than the standard one.