A novel hybrid multi-objective bacterial colony chemotaxis algorithm

Bacterial colony chemotaxis algorithm was originally developed for optimal problem with continuous space. In this paper the discrete bacterial colony chemotaxis (DBCC) algorithm is developed to solve multiobjective optimization problems. The basic DBCC algorithm has the disadvantage of being trapped into the local minimum. Therefore, some improvements are adopted in the new algorithm, such as adding chaos transfer mechanism when the bacterium choose their next locations and the crowding distance operation to maintain the population diversity in the Pareto Front. The definition of chaos transfer mechanism is used to retain the elite solution produced during the operation, and the definition of crowding distance is used to guide the bacteria for determinate variation, thus enabling the algorithm obtain well-distributed solution in the Pareto optimal set. The convergence properties of the DBCC strategy are tested on some test functions. At last, some numerical results are given to demonstrate the effectiveness of the results obtained by the new algorithm.

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant.Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry.Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD).In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE).The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed.The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions.To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE.Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms [Non-dominated sorting genetic algorithm (NSGA-II), Speed-constrained multi-objective particle swarm optimization (SMPSO) and generalized differential evolution (GDE3)] when corner solutions are inserted into the population at different evolutionary stages. The problem of finding corner solutions is addressed by proposing a new algorithm based in multi-objective particle swarm optimization (MOPSO). Results concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5) and respective analysis are presented.

The prediction of liquefaction susceptibility for highly unbalanced database with limited and important input parameters is a crucial issue. The proposed multi-objective feature selection algorithms (MOFS) were applied to highly unbalanced databases of in situ tests: standard penetration test (SPT), cone penetration test (CPT) and shear wave velocity (Vs) test. Two multi-objective algorithms, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective symbiotic organisms search algorithm (MOSOS), were coupled with learning algorithms, artificial neural network (ANN) and multivariate adaptive regression spline (MARS) separately to effectively select the optimal parameters and simultaneously minimize the error. The obtained optimal point has approximately equal accuracy in both liquefiable and non-liquefiable conditions for training and testing. The important inputs found for models based on SPT are: (N1)60, amax and Mw; CPT: qc1, amax and CSR and Vs: Vs1, CSR, amax and Mw. The CPT-based models were found to be the most efficient.

This article is to presents an improved differential evolution (DE) via memory-based mechanism of particle swarm optimization (PSO). Due to uses the memory concept of PSO, the proposed DE is termed as ‘memory-based DE’ where new mutation and crossover operators are introduced. This proposed technique is validated on three typical benchmark functions namely, Rosenbrock, Rastrigrin and Griewank, and then implemented on three different test systems (3, 6 and 15 unit) of economic dispatch (ED) problem. Experimental results prove that the proposed technique produces faster and more accurate solutions than traditional DE and PSO with the other state-of-the-art algorithms.

Particle swarm optimization(PSO) algorithm has advantages of fast search velocity, high efficiency, and simple implementation. However, PSO is troubled with the premature problem. In this paper, two premature mechanisms were combined with a mutation strategy. Variance particle swarm optimization algorithm and evolutionary factor particle swarm optimization algorithm were compared to study effects of different premature mechanisms. 4 cases of economic load dispatch (ELD) problems were selected to evaluate the performance of VPSO and EFPSO. Results show that VPSO not only can obtain more accurate solutions than EFPSO, but also exhibits stronger stability than EFPSO on solving ELD problems. In addition, EFPSO converges faster than VPSO.

Developing and optimizing fuzzy relation equations are of great relevance in system modeling, which involves analysis of numerous fuzzy rules. As each rule varies with respect to its level of influence, it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance. In this study, we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules. The subset of rules is selected by maximizing their performance of the obtained solutions. The originality of this study is conducted in the following ways. Starting with developing granular fuzzy relation equations, an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules (the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations), which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency. Then, the particle swarm optimization (PSO) is implemented to solve a multi-objective optimization problem, in which not only an optimal subset of rules is selected but also a parameter ε for specifying a level of information granularity is determined. A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance. A visible improvement of particle swarm optimization (about 78.56% of the encoding mechanism of particle swarm optimization, or 90.42% of particle swarm optimization with an exploration operator) is gained over the method conducted without using the particle swarm optimization algorithm.

An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance

Under the environment of smart grid, residential users play an extremely key to improve the efficiency of the power grid. It is necessary to motivate residential users to participate in managing their household appliances. However, it is a considerable challenge for residential users to find an optimal scheduling scheme of household appliances by themselves. In this paper, a novel model for home energy management system (HEMS) based on Internet of things (IoT) is proposed to solve this challenge. The model not only utilizes radio frequency technology (RFID) to achieve full automatic control of household appliances, but also employs renewable energy, storage battery and plug-in electric vehicles (PEV). Considering various users' demands, the model can set four kinds of demands for household appliances via terminal appliances. To provide a higher users' satisfaction, two types of satisfactory functions are presented. The model adopts the time interval optimization strategy to find an optimal scheme, and the objective function minimizes the users' electricity costs as well as maximizes the users' satisfaction. Meanwhile the discrete multi-objective bacterial colony chemotaxis algorithm (DMOBCC) is adopted to solve this problem. Case studies are implemented to demonstrate the time interval optimization strategy and simulation results verify the effectiveness of the proposed approach.

In this paper, a multi-objective scenario based mathematical model is presented for the capacitated hub location problem in public transportation considering economic and investment aspects. Three objective functions are regarded in the presented mathematical model. The first one aims to minimise the total costs considering the possibility of investing the unused budget at each period. The second one aims to minimise the total processing time in the hub network at each period. The last one aims to minimise the maximum distance between each pair of origin-destination nodes in the network. To solve the model, three multi-objective meta-heuristic algorithms are developed, namely S metric selection evolutionary multi-objective optimisation algorithm (SMS-EMOA), multi-objective imperialist competitive algorithm (MOICA) and non-dominated sorting genetic algorithm (NSGA-II). Finally, developed algorithms are compared to each other based on several comparison measures using a relatively novel statistical approach.

This chapter describes the use of a multiobjective memetic algorithm in aerodynamic shape optimization applications using computational fluid dynamics(CFD). A systematic overviewon multiobjective optimization is given. It covers parameterized scalarization, population-based Pareto front forming using evolutionary algorithms, formulation of Pareto front improvement using local search methods, and various possible heuristics that can be applied in formulating efficient memetic algorithms. A constrained local search, combined with a non-dominated sorting genetic algorithm with niching in both objective and design space is proposed in the paper. Test function results are given and finally the method is used to solve the robust airfoil design problem using a multi-point formulation.

With the increasing development of clean energy technologies, the application of new energy and carbon capture technology would have a positive effect for CO2 emissions reduction. In this paper, planning of power system model including clean energy technology facing low carbon targets established, considering the carbon emission rights allocation and carbon trading. By studying coal-fired power plant planning model with wind power and carbon capture technology for the future planning period, establish the multi-objective model of minimum comprehensive cost and biggest carbon trading gains, considering constraint conditions of power generating capacity, wind power integrated capacity and carbon emissions reduction targets. Use the bacterial colony chemotaxis algorithm for optimization calculation of the power planning, to give a planning scheme that can meet the requirements of economic development trend and emission reducing requirements.

The uncertainty of wind farm power and load both have a certain impact on the economic dispatch of the power system. First, this study deals with the fuzzy processing of power prediction error and load forecasting error of wind farms based on fuzzy random theory. On this basis, a multi-objective fuzzy stochastic dispatch model of a power system with flexible load is established under the carbon trading mechanism. And, the nonlinear cost of flexible load response and the cost of carbon emission compensation is introduced to the multi-objective function of the model. In addition, the fuzzy chance constraint of the spinning reserve is added to the constraint conditions. By introducing the variable, the fuzzy random dispatch model is transformed into a clear equivalent model. Finally, the discrete bacterial colony chemotaxis algorithm is used to process the model, and the optimal solution to the multi-objective function is obtained by a compromise strategy based on a small degree of satisfaction. In the simulation, a classic IEEE10 system and a wind farm are taken as examples. The results show that compared with the other three traditional dispatch models, the total power generation cost is reduced by US$13 670, US$5610 and US$86 010, respectively using the model proposed in this article.

Considering the competition between rescue points, we use artificial intelligence (AI) driven Internet of Thing (IoT) and regional material storage data to propose a multiobjective scheduling algorithm of flood control materials based on Pareto artificial bee colony (MSA_PABC). To address the scheduling of flood control materials, the multiple types of flood control materials, the multiple disaster sites, and entertain both emergency and fairness of rescue need to be considered comprehensively. The MSA_PABC has the constraints such as storage quantity constraint of warehouse materials, material demand constraint, and maximum transportation distance of flood control materials. We establish the scheduling optimization model of flood control materials for each disaster rescue point and the total scheduling optimization model for all flood control materials. Then, MSA_PABC uses the modified Pareto artificial bee colony algorithm to solve the multiobjective models. Three types of initialization strategies are proposed to calculate the fitness of each rescue point and the overall evaluation value of the food source. We propose the employ bee operations such as niche technology and local search of the variable neighborhood, the onlooker bee operations such as Pareto nondominated sorting and crossover operation, the scout bee operations such as maximum evolutionary threshold, and end elimination mechanism. Finally, our proposed solution obtains the nondominated solution set and its optimal solution. The experimental results show that no matter how the number of rescue points changes, MSA_PABC can find the nondominated solution set and optimal solution quickly. It improves the convergence rate of MSA_PABC and material satisfaction rate. Our solution also reduces the average maximum transportation distance, the standard deviation of maximum transportation distance, and the standard deviation of material satisfaction rate. The evaluation also demonstrates MSA_PABC outperforms the state‐of‐arts such as ABC (artificial bee colony), NSGA2 (nondominated sorting genetic algorithm 2), and MOPSO (multiobjective particle swarm optimization).