Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality...
详细信息
Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-basedincrementallearning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.
作者：
Xue, XingsiChen, JunfengFujian Univ Technol
Coll Informat Sci & Engn Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol
Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol
Fujian Key Lab Automot Elect & Elect Drive Fuzhou 350118 Fujian Peoples R China Hohai Univ
Coll IOT Engn Changzhou 213022 Jiangsu Peoples R China
Ontology matching is an effective technique to solve the ontology heterogeneous problem in Semantic Web. Since different ontology matchers do not necessarily find the same correct correspondences, usually several comp...
详细信息
Ontology matching is an effective technique to solve the ontology heterogeneous problem in Semantic Web. Since different ontology matchers do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of entities in order to increase evidence towards a potential match or mismatch. How to select, combine and tune various ontology matchers to obtain the high quality ontology alignment is one of the main challenges in ontology matching domain. Recently, Evolutionary algorithms (EA) has become the most suitable methodology to face this challenge, however, the huge memory consumption, slow convergence and premature convergence limit its application and reduce the solution's quality. To this end, in this paper, we propose a Hybrid population-based incremental learning algorithm (HPBIL) to automatically select, combine and tune different ontology matchers, which can overcome three drawbacks of EA based ontology matching techniques and improve the ontology alignment's quality. In one hand, HPBIL makes use of a probabilistic representation of the population to perform the optimization process, which can significantly reduce EA's the memory consumption and the possibility of the premature convergence. In the other, we introduce the local search strategy into PBIL's evolving process to trade off its exploration and exploitation, and this marriage between global search and local search is helpful to reduce the runtime. In the experiment, we utilize different scale testing cases provided by the Ontology Alignment Evaluation Initiative (OAEI 2016) to test HPBIL's performance, and the experimental results show that HPBIL's results significantly outperform other EA based ontology matching techniques and top-performers of the OAEI competitions.
An improved population-basedincrementallearning (IPBIL) algorithm is proposed to plan collision-free cutting paths of multi-bridge water-jet cutting processes. Multi-bridge waterjet cutting machines (MWCM) are one o...
详细信息
ISBN:
(纸本)9781479931064
An improved population-basedincrementallearning (IPBIL) algorithm is proposed to plan collision-free cutting paths of multi-bridge water-jet cutting processes. Multi-bridge waterjet cutting machines (MWCM) are one of the preferred solutions for cutting large-size flat work pieces. The work areas of two adjacent bridges with a waterjet head are designed to overlap with each other in an MWCM to ensure no dead zones of cutting. It means that a pair of adjacent bridges may crash with each other over their overlapped area and result in damages of the machine. It is an interference problem that one must solve for MWCM. Due to a great number of curves to be cut in a large process area of MWCM, it needs to optimize the cutting routes of the bridges. This paper proposes an IPBIL-based integrated method for solving both the interference and routing problems. The validity of the presented method is confirmed with a case study.
An improved population-based incremental learning algorithm, in short IPBIL, is proposed to solve thevehicle routing problem with soft time windows (VRPSTW) with an objective to minimize the count of vehicles as well ...
详细信息
ISBN:
(纸本)9781479931064
An improved population-based incremental learning algorithm, in short IPBIL, is proposed to solve thevehicle routing problem with soft time windows (VRPSTW) with an objective to minimize the count of vehicles as well as the total travel distance. VRPSTW is subject to the soft time window constraint that allows to be violated but with penalty. In this paper, the constraint is embedded into a probability selection function and the original probability model of population-basedincrementallearning (PBIL) algorithm becomes 3-dimensional. This improvement guarantees that the population search of individuals is more effective by escaping from a bad solution space. Simulation of Solomon benchmark shows that the results average vehicle counts with IPBIL is reduced very significantly contrasted to those with Genetic algorithm (GA) and PBIL, respectively. Both the average travel length and total time window violations by IPBIL are the least among these tested methods. IPBIL is more effective and adaptive than PBIL and GA.
This paper proposes a self-adaptive hybrid population-based incremental learning algorithm (SHPBIL) for the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP) with makespan criterion. At the initia...
详细信息
ISBN:
(纸本)9783642394799;9783642394782
This paper proposes a self-adaptive hybrid population-based incremental learning algorithm (SHPBIL) for the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP) with makespan criterion. At the initial phase of SHPBIL, the information entropy (IE) of the initial population and an Interchange-based search are utilized to guarantee a good distribution of the initial population in the solution space, and a training strategy is designed to help the probability matrix to accumulate information from the initial population. In SHPBIL's global exploration, the IE of the probability matrix at each generation is used to evaluate the evolutionary degree, and then the learning rate is adaptively adjusted according to the current value of IE, which is helpful in guiding the search to more promising regions. Moreover, a mutation mechanism for the probability model is developed to drive the search to quite different regions. In addition, to enhance the local exploitation ability of SHPBIL, a local search based on critical path is presented to execute the search in some narrow and promising search regions. Simulation experiments and comparisons demonstrate the effectiveness of the proposed SHPBIL.
In this paper, a hybrid population-based incremental learning algorithm (HPBIL) is proposed for solving the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP). The objective function is to minimize...
详细信息
ISBN:
(纸本)9783037856482
In this paper, a hybrid population-based incremental learning algorithm (HPBIL) is proposed for solving the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP). The objective function is to minimize the maximum completion time (i.e., makespan). In HPBIL, the PBIL with a proposed Insert-based mutation is used to perform global exploration, and an Interchange-based neighborhood search with first move strategy is designed to enhance the local exploitation ability. Computational experiments and comparisons demonstrate the effectiveness of the proposed HPBIL.
To improve the computational efficiency of Monte Carlo simulation in composite power systems reliability evaluation, this study presents a method based on the improved estimation of distribution algorithm (EDA) and do...
详细信息
To improve the computational efficiency of Monte Carlo simulation in composite power systems reliability evaluation, this study presents a method based on the improved estimation of distribution algorithm (EDA) and double cross linked list. Compared to traditional techniques, this method is comprehensively improved in the stage of both sampling and state evaluation. In the sampling stage, the population-based incremental learning algorithm is presented, where the probability vector is updated based on the distribution characteristics of excellent samples in population of previous generations. Meanwhile, setting a limit to the probabilities of elements in normal state and mutation strategy are introduced, which improves the excellent characteristics of the population. In the state evaluation stage, the state search and match process is speed up by utilising the intelligent storage technology based on the double across linked list. It avoids calling the optimal power flow for the same state repeatedly. Finally, the proposed method is tested in IEEE RTS 79. As the result shows, compared with other methods ever used in reliability evaluation, this method is not only more efficient in computation but also more accurate. Thus, the proposed method is proved to be reliable and effective.
A portfolio selection problem is about finding an optimal scheme to allocate a fixed amount of capital to a set of available assets. The optimal scheme is very helpful for investors in making decisions. However, findi...
详细信息
ISBN:
(纸本)9780769542539
A portfolio selection problem is about finding an optimal scheme to allocate a fixed amount of capital to a set of available assets. The optimal scheme is very helpful for investors in making decisions. However, finding the optimal scheme is difficult and time-consuming especially when the number of assets is large and some actual investment constraints are considered. This paper proposes a new approach based on estimation of distribution algorithms (EDAs) for solving a cardinality constrained portfolio selection (CCPS) problem. The proposed algorithm, termed PBILCCPS, hybridizes an EDA called population-basedincrementallearning (PBIL) algorithm and a continuous PBIL (PBILc) algorithm, to optimize the selection of assets and the allocation of capital respectively. The proposed algorithm adopts an adaptive parameter control strategy and an elitist strategy. The performance of the proposed algorithm is compared with a genetic algorithm and a particle swarm optimization algorithm. The results demonstrate that the proposed algorithm can achieve a satisfactory result for portfolio selection and perform well in searching nondominated portfolios with high expected returns.
In this paper, we introduce a new stochastic Location-Allocation Problem which assumes the movement of customers over time. We call this new problem Dynamic Customer Location-Allocation Problem (DC-LAP). The problem i...
详细信息
ISBN:
(纸本)9781728121536
In this paper, we introduce a new stochastic Location-Allocation Problem which assumes the movement of customers over time. We call this new problem Dynamic Customer Location-Allocation Problem (DC-LAP). The problem is based on the idea that customers will change locations over a defined horizon and these changes have to be taken into account when establishing facilities to service customers demands. We generate 1440 problem instances by varying the problem parameters of movement rate which determines the possible number of times a customer will change locations over the defined period, the number of facilities and the number of customers. We propose to analyse the characteristics of the instances generated by testing a search algorithm using the stochastic dynamic evaluation (based on the replication of customer movement scenarios) and a deterministic static evaluation (based on the assumption that customer will not move over time). We show that the dynamic approach obtains globally better results, but the performances are highly related to the parameters of the problem. Moreover, the dynamic approach involves a significantly high computational overhead.
The Uncapacitated Location-Allocation problem (ULAP) is a major optimisation problem concerning the determination of the optimal location of facilities and the allocation of demand to them. In this paper, we present t...
详细信息
ISBN:
(纸本)9781509060177
The Uncapacitated Location-Allocation problem (ULAP) is a major optimisation problem concerning the determination of the optimal location of facilities and the allocation of demand to them. In this paper, we present two novel problem variants of Non-Linear ULAP motivated by a real-world problem from the telecommunication industry: Uncapacitated Location-Allocation Resilience problem (ULARP) and Uncapacitated Location-Allocation Resilience problem with Restrictions (ULARPR). Problem sizes ranging from 16 to 100 facilities by 50 to 10000 demand points are considered. To solve the problems, we explore the components and configurations of four Genetic algorithms [1], [2], [3] and [4] selected from the ULAP literature. We aim to understand the contribution each choice makes to the GA performance and so hope to design an Optimal GA configuration for the novel problems. We also conduct comparative experiments with population-basedincrementallearning (PBIL) algorithm on ULAP. We show the effectiveness of PBIL and GA with parameter set: random and heuristic initialisation, tournament and fined_grained tournament selection, uniform crossover and bitflip mutation in solving the proposed problems.
暂无评论