|
|
|
| Improved cuckoo search algorithm for multi-objective optimization problems |
| YANG Hui-hua1,2 , XIE Pu-mo1, ZHANG Xiao-feng1, MA Wei1, LIU Zhen-bing3 |
| 1. Guangxi Experiment Center of Information Science, Guilin University of Electronic Technology, Guilin 541004, China;2. Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China; 3. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China |
|
|
|
Abstract In trying to solve multi-objective optimization problems, an improved multi-objective cuckoo search algorithm (IMOCS) was introduced. Compared with the standard multi-objective cuckoo search algorithm (MOCS), the IMOCS had two improvements, used a dynamic adaptive step-size control amount in Lévy flight; chose the next Lévy flight population based on the level and crowding distance. To verify the effectiveness of the IMOCS, test instances (SCH, ZDT series, LZ) were used to evaluate the performance: the generalized distance between the obtained Pareto front and the true Pareto front, and the diversity of the obtained Pareto front. Our results and comparison with the MOCS, NSPSO and NSGA-II showed that the IMOCS obtains a smaller generalized distance, which meant the IMOCSs Pareto front is closer to the true Pareto front, and at the same time its Pareto front distributes broader and more uniform, that diversity is better.
|
|
Published: 01 August 2015
|
|
|
求解多目标优化问题的改进布谷鸟搜索算法
针对求解多目标优化问题, 提出一种改进的多目标布谷鸟搜索算法(IMOCS).相比于标准多目标布谷鸟搜索算法(MOCS),IMOCS在莱维飞行中使用动态自适应的步长控制量,并基于层级和拥挤度距离选择下一次莱维飞行的种群.为了验证算法的有效性,通过在测试实例(SCH,ZDT系列,LZ)计算所求Pareto前沿与真实Pareto前沿的广义距离和所求Pareto前沿的多样性来测试IMOCS的性能.结果表明,与MOCS, NSPSO, NSGA-II比较,IMOCS所求的广义距离更小,即由IMOCS所求Pareto前沿更加接近于真实Pareto前沿,同时IMOCS的Pareto前沿分布更加广泛和均匀,即多样性更好.
|
|
| ［1］ YANG X S, DEB S. Cuckoo search via Lévy flights ［C］∥World Congress on Nature &Biologically Inspired Computing. NaBic: IEEE Publications, 2009: 210-214.
［2］ FISTER J I, YANG X S, FISTER D, et al. Cuckoo search: A brief literature review ［M］. London: Springer International Publishing, 2014: 49-62.
［3］ YANG X S, DEB S. Cuckoo search: recent advances and applications ［J］. Neural Computing and Applications, 2014, 24(1): 169-174.
［4］ GHERBOUDJ A, LAYEB A, CHIKHI S. Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm ［J］. International Journal of Bio-Inspired Computation, 2012, 4(4): 229-236.
［5］ OUAAARAB A, AHIOD B, YANG X S. Discrete cuckoo search algorithm for the travelling salesman problem ［J］. Neural Computing and Applications, 2014, 24(7/8): 1659-1669.
［6］ LI Xiang-tao, WANG Jia-nan, YIN Ming-hao. Enhancing the performance of cuckoo search algorithm using orthogonal learning method ［J］. Neural Computing and Applications, 2014, 24(6): 1233-1247.
［7］ 王李进, 尹义龙, 钟一文. 逐维改进的布谷鸟搜索算法 ［J］. 软件学报, 2013, 24(11): 2687-2698.
WANG Li-jin, YIN Yi-long, ZHONG Yi-wen. Cuckoo search algorithm with dimension by dimension improvement ［J］. Journal of Software, 2013, 24(11): 2687-2698.
［8］ SRIVASTAVA P R, KHANDELWAL R, KHANDELWAL S. Automated test data generation using cuckoo search and tabu search (CSTS) algorithm ［J］. Journal of Intelligent Systems, 2012, 21(2): 195-224.
［9］ 胡欣欣, 尹义龙. 求解函数优化问题的合作协同进化布谷鸟搜索算法 ［J］. 模式识别与人工智能, 2013, 26(11): 1041-1049.
HU Xin-xin, YIN Yi-long. Cooperative co-evolutionary cuckoo search algorithm continuous function optimization poblems ［J］. Pattern Recognition and Artificial Intelligence, 2013, 26(11): 1041-1049.
［10］ YANG X S, DEB S. Multi-objective cuckoo search for design optimization ［J］. Computers & Operations Research, 2011, 40(6): 1616-1624.
［11］ LAYEB A, LAHOUESNA N, KIRECHE B. A multi-objective binary cuckoo search for bicriteria knapsack problem ［J］. International Journal of Information Engineering & Electronic Business, 2013, 5(4): 8-15.
［12］ CHANDRASEKARAN K, SIMON S P. Multi-objective scheduling problem: Hybrid approach using fuzzy assisted cuckoo search algorithm ［J］. Swarm and Evolutionary Computation, 2012, 5(1): 1-16.
［13］ BCK T. Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms ［M］. Oxford: Oxford University Press, 1996: 161-164.
［14］ 张永韡, 汪镭, 吴启迪. 动态适应布谷鸟搜索算法 ［J］. 控制与决策, 2014, 29(4): 617-622.
ZHANG Yong-wei, WANG Lei, WU Qi-di. Dynamic adaptation cuckoo search algorithm ［J］.Control and Decision, 2014, 29(4): 617-622.
［15］ SRINIVAS N, DEB K. Multi-objective optimization using non-dominated sorting in genetic algorithms ［J］. Evolutionary computation, 1994, 2(3): 221-248.
［16］ DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II ［J］. Evolutionary Computation, IEEE Transactions on, 2002, 6(2): 182-197.
［17］ SCHAFFER J D. Multiple objective optimization with vector evaluated genetic algorithms ［C］∥Proceedings of the 1st international Conference on Genetic Algorithms. New Jersey: L. Erlbaum Associates Inc, 1985: 93-100.
［18］ ZITZLER E, THIELE L. Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach ［J］. Evolutionary Computation, IEEE Transactions on, 1999, 3(4): 257-271.
［19］ ZITZLER E, DEB K, THIELE L. Comparison of multi-objective evolutionary algorithms: Empirical results ［J］. Evolutionary Computation, 2000, 8(2): 173-195.
［20］ ZHANG Qing-fu, LI Hui. MOEA/D: A multi-objective evolutionary algorithm based on decomposition ［J］. Evolutionary Computation, IEEE Transactions on, 2007, 11(6): 712-731.
［21］ LI Xiao-dong. A non-dominated sorting particle swarm optimizer for multi-objective optimization ［C］∥Genetic and Evolutionary Computation—GECCO 2003. Chicago: Springer Berlin Heidelberg, 2003: 37-48.
［22］ LI Hui, ZHANG Qing-fu. Multi-objective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II ［J］. Evolutionary Computation, IEEE Transactions on, 2009, 13(2): 284-302.
［23］ DEB K Multi-objective optimization using evolutionary algorithms ［M］. Chichester: John Wiley & Sons, 2001: 320-332. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
