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A balanced fuzzy Cultural Algorithm with a modified Levy flight search for real parameter optimization

INFORMATION SCIENCES 2018年 447卷 12-35页

作者： Ali, Mostafa Z. Awad, Noor H. Reynolds, Robert G. Suganthan, Ponnuthurai N. Jordan Univ Sci & Technol Scool Comp & Informat Technol Irbid 22710 Jordan Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Wayne State Univ Sch Engn Detroit MI 48202 USA

Over the last few decades, a plethora of improved evolutionary algorithms was developed with exquisite performance on numerical and real-world problems. Among such algorithms, the Cultural Algorithm is a hyper-heuristic evolutionary algorithm, which explicitly utilizes the knowledge represented in the belief space as an essential component to guide the evolutionary search, In this paper, a new enhanced Cultural Algorithm incorporates a fuzzy system with a modified Levy flight search that is introduced as a new component. The new algorithm namely, b-fCA+mLF, utilizes a balanced search mode using a customized belief space with a quality function to harmonize how the knowledge sources work in parallel. The communication protocols between the population space and the belief space are established through the modified fuzzy acceptance and influence functions. Using these new functions, the best individuals are selected to create new knowledge in an effective manner. Similarly, the best knowledge is selected to evolve the individuals in the population space and guide the evolutionary search towards the promising regions. A modified Levy flight search is proposed and utilizes the information from the belief space as an input to support the evolution process to generate better solutions. The algorithm is tested on the benchmark suite taken from the IEEE-CEC'15 competition on learning-based real-parameter single objective optimization, and is compared with other algorithms including the best performer algorithms in this competition. The results suggest that the proposed algorithm is statistically better and is able to produce higher quality solutions than the other state-of-the-art algorithms. A case study on the well-known 120-bar dome truss design problem is also presented to test the validity of the proposed algorithm for the solution of complex design problems. The results of this problem show the ability of the proposed algorithm to generate good solutions with fewer fun

关键词： Evolutionary algorithm Cultural Algorithm Fuzzy system Knowledge source Levy flight single objective optimization

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Challenges with multi-objective QSAR in drug discovery

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EXPERT OPINION ON DRUG DISCOVERY 2018年第9期13卷 851-859页

作者： Lambrinidis, George Tsantili-Kakoulidou, Anna Univ Athens Dept Pharm Div Pharmaceut Chem Athens 15771 Greece

Introduction: The complexity in the drug discovery pipeline, in combination with the exponential growth of experimental and computational data, the technological achievements, and the access to large data sets, has led to a continuous evolution and transformation of quantitative structure-activity relationships (QSAR) to compete with the challenges of multi-objective drug discovery. Areas covered: After a short overview of the multiple objectives involved in drug discovery, this review focuses on definition of the drug-like space and the construction of local and/or global models, platforms and workflows for step-by-step single-objective optimization (SOO) of the different and often conflicting processes. Multi-targeted drug design is a particular case of multi-objective QSAR integrated into the new era of polypharmacology. Multi-objective optimization (MOO), based on desirability functions or Pareto surfaces and its application in QSAR, as an alternative optimization philosophy, is also discussed. Expert opinion: Access to large databases as well as to software services by means of cloud technology facilitates research for more efficient and safer drugs. QSAR models implemented in web platforms and workflows provide sequential SOO for multiple biological and toxicity end points, while MOO, still restricted to a limited number of objectives, is helpful for multi-target or selectivity design, as well as for model prioritization.

关键词： QSAR multi-objective drug design multi-target drug design drug-like space single objective optimization multi-objective optimization

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Self-adapting self-organizing migrating algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 51卷 100593-000页

作者： Skanderova, Lenka Fabian, Tomas Zelinka, Ivan VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Comp Sci Ostrava 70800 Czech Republic Ton Duc Thang Univ Fac Elect & Elect Engn Modeling Evolutionary Algorithms Simulat & Artifi Ho Chi Minh City Vietnam

The self-organizing migrating algorithm is a population-based algorithm belonging to swarm intelligence, which has been successfully applied in several areas for solving non-trivial optimization problems. However, based on our experiments, the original formulation of this algorithm suffers with some shortcomings as loss of population diversity, premature convergence, and the necessity of the control parameters hand-tuning. The main contribution of this paper is the development of the novel algorithm mitigating the mentioned issues of the original self-organizing migrating algorithm. We have applied the ideas of the self-adaptation of the control parameters, the different principle of the leader creation, and the external archive of the successful particles. For some special cases, we are able to utilize the differential grouping to detect the interacting variables effectively removing the need for the perturbation parameter. To prove the efficiency of the novel algorithm, we have performed experiments on fifteen unconstrained problems from the CEC 2015 benchmark. The algorithm is compared with seven well-known evolutionary and swarm algorithms. The results of the experiments indicate that the mechanisms used in the novel algorithm had significantly improved the performance of the original self-organizing migrating algorithm, and the new algorithm can now compete with the selected algorithms.

关键词： Self-organizing migrating algorithm Swarm intelligence Differential grouping Control parameters adaptation single objective optimization

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Thermo-economic analysis and optimization of a novel carbon dioxide based combined cooling and power system

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ENERGY CONVERSION AND MANAGEMENT 2019年 199卷 112048-000页

作者： Li, Bo Wang, Shun-sen Xi An Jiao Tong Univ Sch Energy & Power Engn Inst Turbomachinery State Key Lab Multiphase Flow Power Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

Supercritical carbon dioxide is widely used in power plant and refrigeration system as working fluid. In this paper, a novel combined cooling and power system consists of supercritical carbon dioxide recompression Brayton cycle and transcritical carbon dioxide refrigeration cycle is proposed. In the novel system, the working fluid through the recompressor in supercritical carbon dioxide recompression Brayton cycle is partially replaced by the refrigeration compressor outlet working fluid in transcritical carbon dioxide refrigeration cycle. Mathematical models are established to simulate the combined system under steady-state conditions and the simulated results show that the reactor dominates the exergy destruction followed by the cooler and high temperature recuperator. Effects of five parameters, including turbine discharge pressure, cooler outlet temperature, evaporation temperature, cooling capacity and mass flow fraction are evaluated. The performances of the proposed combined system and the separation system are optimized and then compared from the perspective of thermodynamics. The calculated results show that the combined system's comparative advantage over separation system will increase with increasing cooling capacity and decreasing evaporation temperature. The exergy efficiency of combined system is up to 2.45% and 5.87% higher than separation system when evaporation temperature is 273.15 K and 253.15 K, respectively. In order to balance the contradiction between the investment and system performance, the multi-objective optimization is conducted with exergy efficiency (to be maximized) and annual cost per heat consumption (to be minimized) as objective functions at different evaporation temperature. Non-dominated sorting generic algorithm-II is employed and Pareto frontier solution for the proposed system is given. Suggestions for engineering practice are provided based on the distributions of some key parameters on Pareto frontier solution.

关键词： Combined cooling and power system Supercritical carbon dioxide recompression Brayton cycle Transcritical carbon dioxide refrigeration cycle Thermo-economic analysis single objective optimization Multi-objective optimization

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Hybrid single Parent-Offspring MVMO for Solving CEC2018 Computationally Expensive Problems

Hybrid Single Parent-Offspring MVMO for Solving CEC2018 Comp...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Rueda, Jose L. Erlich, Istvan Delft Univ Technol Dept Elect Sustainable Energy Delft Netherlands Univ Duisburg Essen Inst Elect Power Syst Duisburg Germany

ISBN: (纸本)9781509060177

Mean-Variance Mapping optimization (MVMO) belongs to the family of evolutionary algorithms, and has proven to be competitive in solving computationally expensive problems proposed in the Icompetitions CEC2014, CEC2015, and CEC2016. MVMO can tackle such problems by evolving a set of solutions (population based approach) or a single solution (single parent-offspring approach). The evolutionary mechanism of MVMO performs within a normalized search space in the range [0, 1]. The power of MVMO stems from its ability based on statistical analysis of the evolving solution based on a mapping function- to adaptively shift the search priority from exploration to exploitation. This paper introduces a newly defined mapping function as well as a new rule for using an embedded local search strategy, and presents several tests conducted by using the test bed of the CEC2018 competition. Numerical results indicate significant improvements on the results obtained in CEC2016 competition.

关键词： Computationally expensive optimization heuristic optimization mean-variance mapping optimization single objective optimization

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Renewable energy system optimization of low/zero energy buildings using single-objective and multi-objective optimization methods

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ENERGY AND BUILDINGS 2015年 89卷 61-75页

作者： Lu, Yuehong Wang, Shengwei Zhao, Yang Yan, Chengchu Hong Kong Polytech Univ Dept Bldg Serv Engn Kowloon Hong Kong Peoples R China

Low energy buildings and zero energy buildings have attracted increasing attention in both academic and professional fields. The performances of these buildings are largely affected by the design of the renewable energy systems. This paper presents a comparison study on two design optimization methods for renewable energy systems in these buildings, including a single objective optimization using Genetic Algorithm and a multi-objectives optimization using Non-dominated Sorting Genetic Algorithm (NSGA-II). Building energy system models and renewable energy system models are developed and adopted, allowing the consideration of the interaction between building energy systems and renewable energy systems in optimization. Two case studies are conducted to evaluate the capability and effectiveness of proposed optimization methods, based on the Hong Kong Zero Carbon Building. The performances of the buildings with the renewable energy systems optimized by both methods are much better than that of the benchmark building in most scenarios. The single objective optimization can provide the "best" solution directly for a given objective while the multi-objective optimization provides rich information for designers to make better compromised decisions. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Low energy building Zero energy building Renewable energy system Genetic Algorithm NSGA-II single objective optimization Multi-objectives optimization

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Simultaneous Heat Transfer Search for single objective Real-Parameter Numerical optimization Problem

Simultaneous Heat Transfer Search for Single Objective Real-...

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IEEE Region 10 Conference

作者： Debasis Maharana Prakash Kotecha Department of Chemical Engineering Indian Institute of Technology Guwahati Department of Chemical Engineering Indian Institute of Technology

ISBN: (纸本)9781509025985

In this work, we demonstrate the performance of a variant of the Heat Transfer Search algorithm which considers simultaneous heat transfer through all the three modes. It has been demonstrated on the thirty real parameter single objective, bound constrained benchmark functions that have been provided as part of one of the competition in IEEE Congress on Evolutionary Computation 2016. It was observed that the Simultaneous Heat Transfer Search (SHTS) algorithm has a very low time complexity and is competitive to other algorithms in terms of its ability to determine the optimal solutions.

关键词： single objective optimization CEC2016 Competition Simultaneous Heat Transfer Search Computationally Expensive Numerical optimization

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An Adaptive Multipopulation Differential Evolution With Dynamic Population Reduction

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IEEE TRANSACTIONS ON CYBERNETICS 2017年第9期47卷 2768-2779页

作者： Ali, Mostafa Z. Awad, Noor H. Suganthan, Ponnuthurai Nagaratnam Reynolds, Robert G. Jordan Univ Sci & Technol Comp Informat Syst Irbid 22110 Jordan Princess Sumaya Univ Technol Amman 11941 Jordan Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

Developing efficient evolutionary algorithms attracts many researchers due to the existence of optimization problems in numerous real-world applications. A new differential evolution algorithm, sTDE-dR, is proposed to improve the search quality, avoid premature convergence, and stagnation. The population is clustered in multiple tribes and utilizes an ensemble of different mutation and crossover strategies. In this algorithm, a competitive success-based scheme is introduced to determine the life cycle of each tribe and its participation ratio for the next generation. In each tribe, a different adaptive scheme is used to control the scaling factor and crossover rate. The mean success of each subgroup is used to calculate the ratio of its participation for the next generation. This guarantees that successful tribes with the best adaptive schemes are only the ones that guide the search toward the optimal solution. The population size is dynamically reduced using a dynamic reduction method. Comprehensive comparison of the proposed heuristic over a challenging set of benchmarks from the CEC2014 real parameter single objective competition against several state-of-the-art algorithms is performed. The results affirm robustness of the proposed approach compared to other state-of-the-art algorithms.

关键词： Differential evolution (DE) numerical optimization single objective optimization subpopulations

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Multi-objective optimization for spectrum sharing in cognitive radio networks: A review

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PERVASIVE AND MOBILE COMPUTING 2017年 41卷 106-131页

作者： Ramzan, Muhammad Rashid Nawaz, Nadia Ahmed, Ashfaq Naeem, Muhammad Iqbal, Muhammad Anpalagan, Alagan Ryerson Univ Dept Elect & Comp Engn WINCORE Lab Toronto ON Canada COMSATS Inst Informat Technol Wah Campus Wah Cantt Pakistan

Due to advancements in the wireless networking technologies, a lot of new wireless services are demanding the radio spectrum for their usage. The radio spectrum is considered as a limited natural resource for radio communications, and it became crowded. In fact, this resource is not limited, rather the scarcity of the radio spectrum arises because of inefficient and fixed utilization of this valuable resource. This inefficient and under-utilization of radio spectrum has diverted the attentions of the researchers towards the different techniques in order to access the spectrum dynamically and adaptively. The concept of Cognitive Radio (CR) can be implemented by making the communication devices intelligent, in order to use the available radio spectrum efficiently. The efficient implementation of cognitive radios into the wireless networks gives rise to a tremendous number of optimization problems with multiple objectives. In most of the cases, these objectives are conflicting with each other and need to be addressed carefully. In this article, we will present the importance of multi-objective optimization (MOO) in cognitive radio networks (CRNs). We will discuss about the fundamental differences in single objective optimization (SOO) and MOO techniques, in order to solve MOO problems associated with CRNs. We will differentiate between the possible relations such as, conflicting, supporting and design dependent, among these objectives. A comparative discussion will also be provided about optimization types, solution approaches/algorithms and different combining techniques to combine multiple objectives in order to address MOO problems those comes with CRNs. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Software defined radio Cognitive radio single objective optimization Evolutionary algorithms Multi-objective optimization Conflicting objectives Pareto optimal solutions

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MVMO for Bound Constrained single-objective Computationally Expensive Numerical optimization

MVMO for Bound Constrained Single-Objective Computationally ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Rueda, Jose L. Erlich, Istvan Delft Univ Technol Dept Elect Sustainable Energy Delft Netherlands Univ Duisburg Essen Inst Elect Power Syst Duisburg Germany

ISBN: (纸本)9781479974924

Mean-Variance Mapping optimization (MVMO) is a recent addition to the heuristic optimization field. The main traits of its evolutionary mechanism reside in the adoption of a single parent-offspring pair approach along with a normalized range of the search space for all optimization variables as well as in the use of a special mapping function, which accounts for the actual mean and variance of the normalized optimization variables for mutation operation. MVMO is also open to further extensions and hybridization with other approaches. Along this spirit, this paper surveys the performance of MVMO when executed in its pure algorithmic procedure and when hybridized to include a local search strategy. Numerical experiments are conducted on the IEEE-CEC 2015 optimization test bed on bound constrained single-objective computationally expensive numerical optimization. Remarkably, MVMO proves effective at solving different complex problems within a reduced number of allowed function evaluations.

关键词： Computational expensive optimization heuristic optimization mean-variance mapping optimization single objective optimization

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