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6th International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Huang, Wenqi Zeng, Zhizhong Xu, Ruchu Fu, Zhanghua Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9780769547633;9781467321389

There are a lot of researches focused on the problem of unequal disks packing for its simplicity in definition and plenty of applications. Because it is NP-hard, most of them are heuristics. up to now, one of the best heuristics is iterated Tabu search. in this paper, we enhance this method by making it population based. We maintain a queue for the best k solutions found so far, and at each time, we choose a solution from it according to an elaborately set probability, perturb the solution and optimize it by adaptive Tabu search, finally, update the queue using the result of the optimization. to see the efficiency of our algorithm, we use it to solve a couple of benchmark problems, and the computational results showed its efficiency by beating several world records.

关键词： Tabu search population based algorithm combinatory neighborhoods unequal disks packing NP-hard problem

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An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm

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APPLIED ENERGY 2024年 364卷

作者： Slowik, Adam Cpalka, Krzysztof Xue, Yu Hapka, Aneta Koszalin Univ Technol Dept Elect & Comp Sci Sniadeckich 2 St PL-75452 Koszalin Poland Czestochowa Tech Univ Dept Intelligent Informat Syst Al Armii Krajowej 36 PL-42202 Czestochowa Poland Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Jiangsu Peoples R China

This article discusses the problem of accurate and efficient modeling of photovoltaic (PV) panels. It is a highly nonlinear problem. The following models were considered: a single diode model, a double diode model, a triple diode model, a four diode model, a module model (a poly-crystalline Photowatt-PWP201 module and a mono-crystalline STM6-40/36 module). The article presents a mathematical notation of these models, a detailed interpretation of their individual components, and a comparison of obtained results. To increase the effectiveness of modeling, a new population-based algorithm which can handle complex objective functions and a large number of decision variables was developed. This is important for the problem of identifying the parameters of PV cell models because each evaluation of the objective function requires calculating a set of points that determine the current-voltage characteristics. Moreover, in the considered problem a solution is searched with the use of the trial and error method. The proposed algorithm is called Micro Adaptive Fuzzy Cuckoo Search Optimization (mu AFCSO). The mu AFCSO algorithm uses several new mechanisms that were developed based on our experience with population-based algorithms. The use of these mechanisms has produced very good results in simulations. In the scope of simulation studies, the mu AFCSO algorithm was used for parameter extraction in six PV cell models and was also applied to optimize fifteen typical test functions. The test functions were considered in order to demonstrate that our algorithm can be used to solve typical problems processed using population-based algorithms. The results obtained in this study were compared with the results obtained using well-established algorithms. The results obtained in this work are better or comparable to them.

关键词： Solar photovoltaic model Single diode model Double diode model Triple diode model Four diode model Module model population based algorithm

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population based Local Search for university course timetabling problems

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APPLIED INTELLIGENCE 2014年第1期40卷 44-53页

作者： Abuhamdah, Anmar Ayob, Masri Kendall, Graham Sabar, Nasser R. Taibah Univ Coll Comp Sci & Engn Dept Comp Sci Madinah 41411 Saudi Arabia Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimisat Res Grp DMO Bangi 43600 Selangor Malaysia Univ Nottingham Sch Comp Sci ASAP Res Grp Nottingham NG8 1BB England Univ Nottingham Malaysia Campus Semenyih 43500 Selangor Darul Malaysia

population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.

关键词： Course timetabling problem Metaheuristics population based algorithm Hybrid methods Gravitational emulation

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CAMONET: Moth-Flame Optimization (MFO) based Clustering algorithm for VANETs

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IEEE ACCESS 2018年 6卷 48611-48624页

作者： Shah, Yasir Ali Habib, Hafiz Adnan Aadil, Farhan Khan, Muhammad Fahad Maqsood, Muazzam Nawaz, Tabassam COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock 43600 Pakistan Univ Engn & Technol Taxila Dept Comp Engn Taxila 47080 Pakistan Univ Engn & Technol Taxila Software Engn Dept Taxila 47080 Pakistan

A network aggregated of wirelessly connected vehicles is recognized as vehicular ad hoc networks (VANETs). Clustering in vehicular network is a technique among many others, which targets to improve communication proficiency in VANETs. In each cluster, there is one cluster head (CH) used to manage the whole cluster. All the communications are accomplished by the CHs, i.e., inter-cluster and the intra-cluster communications. The efficiency of a network is measured by number of CHs, load on each CH and lifetime of clusters. In this paper, a novel Clustering algorithm centered on Moth-Flame Optimization for VANETs (CAMONET) is anticipated. This is a nature-inspired algorithm. CAMONET generates optimized clusters for robust transmission. CAMONET is evaluated experimentally with renowned techniques, such as multiobjective particle swarm optimization, clustering algorithm based on ant colony optimization for VANETs, and comprehensive learning particle swarm optimization. To assess the comparative efficiency of these algorithms, numerous experiments are performed. The results are accomplished by modifying the values of grid size of the network, the number of nodes in the network, and the transmission range of nodes. The speed, direction, and transmission range of the nodes are the notable factors considered for optimized clustering. The results indicate that CAMONET delivers near optimal results that develops it into an efficient method to perform vehicular clustering in order to improve the overall performance of the network.

关键词： Intelligent transportation system (ITS) vehicular ad hoc networks (VANETs) ant colony optimization (ACO) moth-flame optimization (MFO) clustering meta heuristic algorithms population based algorithm

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Baldwin Effect based Particle Swarm Optimizer for Multimodal Optimization

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JOURNAL OF COMPUTERS 2012年第9期7卷 2114-2119页

作者： Zhai, Ji Qiang Wang, Ke Qi Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Heilongjiang Peoples R China

Particle Swarm Optimization (PSO) is an effective optimal technique. However, it often suffers from being trapped into local optima when solving complex multimodal optimizing problems due to its inefficient exploiting of feasible solution space. This paper proposes a Baldwin effect based learning particle swarm optimizer (BELPSO) to improve the performance of PSO when solving complex multimodal optimizing problems. This Baldwin effect based learning strategy utilizes the historical beneficial information to increase the potential search range and retains diversity of the particle population to discourage premature. On the other hand, the exemplars provided by Baldwin effect based learning strategy can flatten out the fitness landscape closing to optima and hence guide the search path towards optimal region. Experimental simulations show that BELPSO has a wider search range of feasible solution space than PSO. Furthermore, the performance comparison between BELPSO and amount of population based algorithms on sixteen well-known test problems shows that BELPSO has better performance in quality of solution.

关键词： Particle Swarm Optimization Baldwin effect Swarm intelligence population based algorithm Computational intelligence

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Hybrid Multi-population based Approach for Controllers Structure and Parameters Selection 18th

Hybrid Multi-population Based Approach for Controllers Struc...

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18th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Lapa, Krystian Cpalka, Krzysztof Paszkowski, Jozef Czestochowa Tech Univ Inst Computat Intelligence Czestochowa Poland Univ Social Sci Informat Technol Inst PL-90113 Lodz Poland Clark Univ Worcester MA 01610 USA

ISBN: (纸本)9783030209124;9783030209117

population-based algorithms are used to solve optimization problems. In order to improve their efficiency, new ways of processing the population are investigated. One approach is to use many populations which operations are synchronized with each other. In this article, we propose a new approach in which specified populations are processed using different population algorithms. It has been tested for a known practical problem of selecting the structure and parameters of the controller.

关键词： population based algorithm Multi-population Controller Structure selection Parameters selection

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A Modified DE: population or Generation based Levy Flight Differential Evolution (PGLFDE) 1

A Modified DE: Population or Generation based Levy Flight Di...

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International Conference On Futuristic Trends in Computational Analysis and Knowledge Management

作者： Sharma, Vishnu Prakash Choudhary, Harji Ram Kumar, Sandeep Choudhary, Vikas Govt Engn Coll Ajmer Ajmer Rajasthan India Jagannath Univ Jaipur Rajasthan India Bhagwant Univ Ajmer India

ISBN: (纸本)9781479984336

Researchers can solve Simple optimization problems using various methods. But differential Evolution (DE) is a method(population based) to solve complex optimization problems in some easy steps that is not possible to handle by mathematical optimization methods. For global optimization problems DE is a probabilistic approach. DE when tested over some bench mark functions and real world problems it performed better than some evolutionary algorithms and swarm - intelligence based algorithms. Balancing between Exploration and Exploitation using DE mutation and crossover with control parameters F and CR (fine tunned) is to be done. DE does not complete demand of good convergence and stagnation. DE Explore better but this exploration capability sometimes may skip the true solution and exhibit premature convergence. To improve this we can decrease the step size but this exhibit stagnation. So for Better exploitation, another approach called Memetic algorithm based on levy flight based local search strategy is used with DE. Further to balance exploration and exploitation in local search area in this paper a population or generation based exploitation of local search area in LFDE is proposed that is called PGLFDE. To show better result proposed strategy is tested on some bench mark functions and compare with recent variants of DE.

关键词： Differential Evolution Levy Flight Search Memetic algorithm Nature Inspired algorithm population based algorithm

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Group learning algorithm: a new metaheuristic algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第19期35卷 14013-14028页

作者： Rahman, Chnoor M. Charmo Univ Coll Sci Comp Dept Sulaymaniyah Iraq Amer Univ Iraq Sulaimani Informat Technol Dept Sulaymaniyah Iraq

Metaheuristics are intelligent optimization techniques that lead the searching procedure through utilizing exploitation and exploration. Increasing the number of hard problems with big data sets has encouraged researchers to implement novel metaheuristics and hybrid the existing ones to improve their performance. Hence, in this work, a novel metaheuristic called group learning algorithm is proposed. The main inspiration of the algorithm emerged from the way individuals inside a group affect each other, and the effect of group leader on group members. The two main steps of optimization, exploration and exploitation are outlined through integrating the behaviors of group members and the group leader to complete the assigned task. The proposed work is evaluated against a number of benchmarks. The produced results of classical benchmarks are compared against PSO, GWO, TLBO, BA, ALO, and SSA. In general, compared to other participated algorithms, out of 19 classical benchmarks, the proposed work showed better results in 11. However, the second best algorithm which is SSA performed better in 4 out of 19 benchmarks. To further evaluate the ability of the algorithm to optimize large scale optimization problems CEC-C06 2019 benchmarks are utilized. In comparison to other participated algorithms, the proposed work produced better results in most of the cases. Additionally, the statistical tests confirmed the significance of the produced results. The results are evidence that the proposed algorithm has the ability to optimize various types of problems including large scale optimization problems.

关键词： Optimization Single objective optimization Benchmark Particle swarm optimization Grey wolf optimization algorithm Teaching learning based optimization population based algorithm Evolutionary algorithm

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Adaptation of population Size in Sine Cosine algorithm

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IEEE ACCESS 2021年 9卷 25258-25277页

作者： Al-Faisal, Hala R. Ahmad, Imtiaz Salman, Ayed A. Alfailakawi, Mohammad Gh Kuwait Univ Coll Engn & Petr Comp Engn Dept Safat 13060 Kuwait

Sine Cosine algorithm (SCA) is a newly proposed competent population-based metaheuristic, which has gained a multi-disciplinary interest in solving optimization problems. Like other metaheuristics, the performance of SCA is sensitive to the settings of its parameters and one such parameter is population Size (PS). There is no one population size that fits all;problem uniqueness requires a matching strategy of parameter selection and adaptation. However, in standard SCA and its variants, PS is treated as a user-controlled parameter and no study has explored the effect of PS adaptation on SCA's performance. To fill this research gap, this study investigates and compares the impact of promising strategies for setting and controlling population size, from other metaheuristics, on the performance of the standard SCA and five of its variants in terms of fitness, run-time, and convergence characteristics. Finding the best PS setting for a metaheuristic is a challenging problem since it depends on both the nature of the algorithm used and the problem being solved. Leading PS adaptation techniques considered in this study are linear staircase reduction, iterative halving, reinitialization and incrementation, pulse wave, population diversity, and three parent crossover strategies. A classic set of 23 well-known benchmark functions has been utilized for a fixed number of evaluations to assess the impact of each PS adaptation strategy on the performance of SCA and its variants. Also, non-parametric Wilcoxon's rank sum test is performed to provide a comprehensive view of various PS adaptation strategies' performance with respect to each other in terms of fitness and run-time. Simulation results reveal that proper selection of PS adaptation strategy can further enhance the exploration and exploitation capabilities of SCA and its variants.

关键词： Statistics Sociology Optimization Mathematical model Convergence Search problems Licenses Metaheuristic population based algorithm optimization population size manipulation sine cosine algorithm (SCA)

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Constrained combinatorial optimization of multi-layered composite structures by means of Stud GA with proportionate selection and extinction

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2017年第6期55卷 2239-2257页

作者： Danzi, Francesco Frulla, Giacomo Cestino, Enrico Politecn Torino Dept Mech & Aerosp Engn Cso Duca Abruzzi 24 I-10128 Turin Italy

In this paper a revised algorithm, derived by the Stud GA, to optimize the stacking sequence of layered composite structures is presented. The constrained combinatorial problem is formulated and hence solved by means of the algorithm presented herein. Unlike the Stud GA, the proposed algorithm uses a fitness-proportionate selection to find potential mates to the Stud and a self-adaptive extinction to avoid premature convergence. Elitism is also allowed. Standard benchmark problems have been used to fully assess the validity of the proposed algorithm. Results in terms of reliability and efficiency are compared against three GAs and a Permutation Search (PS) algorithm. It has been demonstrated, by means of a parametric study, that a low probability of extinction is sufficient to ensure fast convergence as well as reliable results. Finally the optimization of a more complex aeronautic structure is addressed aiming to prove the validity of the proposed algorithm in a two level optimization strategy. Results in terms of efficiency are compared against those of an in-house standard GA.

关键词： Buckling load maximization Stacking sequence Combinatorial optimization population based algorithm Composite structures

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