{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:11:13Z","timestamp":1771261873836,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2014,10,28]],"date-time":"2014-10-28T00:00:00Z","timestamp":1414454400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Swarm Intell"],"published-print":{"date-parts":[[2014,12]]},"DOI":"10.1007\/s11721-014-0099-x","type":"journal-article","created":{"date-parts":[[2014,10,27]],"date-time":"2014-10-27T14:53:15Z","timestamp":1414421595000},"page":"275-302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Characterising the searchability of continuous optimisation problems for PSO"],"prefix":"10.1007","volume":"8","author":[{"given":"K. M.","family":"Malan","sequence":"first","affiliation":[]},{"given":"A. P.","family":"Engelbrecht","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,10,28]]},"reference":[{"key":"99_CR1","first-page":"47","volume-title":"Advances in genetic programming","author":"L Altenberg","year":"1994","unstructured":"Altenberg, L. (1994). The evolution of evolvability in genetic programming. In K. Kinnear (Ed.), Advances in genetic programming (pp. 47\u201374). Cambridge, MA: MIT Press."},{"key":"99_CR2","first-page":"57","volume-title":"Proceedings of the Seventh International Conference on Genetic Algorithms","author":"L Altenberg","year":"1997","unstructured":"Altenberg, L. (1997). Fitness distance correlation analysis: An instructive counterexample. In T. Baeck (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms (pp. 57\u201364). San Francisco, CA: Morgan Kaufmann."},{"key":"99_CR3","unstructured":"BBOB Black-Box Optimization Benchmarking. (2013). Comparing Continuous Optimisers. http:\/\/coco.gforge.inria.fr\/ ."},{"key":"99_CR4","unstructured":"Bischl, B., Mersmann, O., Trautmann, H., & Preuss, M. (2012). Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In T. Soule (Ed.), Proceedings of the Fourteenth International Genetic and Evolutionary Computation Conference (pp. 313\u2013320). New York, NY: ACM."},{"key":"99_CR5","doi-asserted-by":"crossref","unstructured":"Borenstein, Y., & Poli, R. (2005a). Information landscapes. In Proceedings of the 2005 Genetic and Evolutionary Computation Conference (pp. 1515\u20131522). New York, NY: ACM.","DOI":"10.1145\/1068009.1068248"},{"key":"99_CR6","doi-asserted-by":"crossref","unstructured":"Borenstein, Y., & Poli, R. (2005b). Information landscapes and problem hardness. In Proceedings of the 2005 Genetic and Evolutionary Computation Conference (pp. 1425\u20131431). New York, NY: ACM.","DOI":"10.1145\/1068009.1068236"},{"issue":"1","key":"99_CR7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s11721-013-0090-y","volume":"8","author":"CW Cleghorn","year":"2014","unstructured":"Cleghorn, C. W., & Engelbrecht, A. P. (2014). A generalized theoretical deterministic particle swarm model. Swarm Intelligence, 8(1), 35\u201359.","journal-title":"Swarm Intelligence"},{"key":"99_CR8","unstructured":"Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micromachine and Human Science (pp. 39\u201343). Piscataway, NJ: IEEE."},{"key":"99_CR9","unstructured":"Eberhart, R., & Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (Vol. 1, pp. 84\u201388). Piscataway, NJ: IEEE."},{"key":"99_CR10","unstructured":"Engelbrecht, A. P. (2012). Particle swarm optimization: Velocity initialization. In: Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1\u20138). Piscataway, NJ: IEEE."},{"key":"99_CR11","unstructured":"Helwig, S., & Wanka, R. (2008). Theoretical analysis of initial particle swarm behavior. In G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, & N. Beume (Eds.), Proceedings of the Tenth International Conference on Parallel Problem Solving from Nature. Lecture Notes in Computer Science (Vol. 5199, pp. 889\u2013898). Berlin: Springer."},{"key":"99_CR12","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/978-3-662-04448-3_18","volume-title":"Theoretical aspects of evolutionary computing","author":"T Jansen","year":"2001","unstructured":"Jansen, T. (2001). On classifications of fitness functions. In L. Kallel, B. Naudts, & A. Rogers (Eds.), Theoretical aspects of evolutionary computing (pp. 371\u2013385). Berlin: Springer."},{"key":"99_CR13","unstructured":"Jones, T., & Forrest, S. (1995). Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Proceedings of the Sixth International Conference on Genetic Algorithms (pp. 184\u2013192). San Francisco, CA: Morgan Kaufmann."},{"key":"99_CR14","unstructured":"Kennedy, J. (1997). The particle swarm: Social adaptation of knowledge. In Proceedings of the IEEE International Conference on Evolutionary Computation (pp. 303\u2013308). Piscataway, NJ: IEEE."},{"key":"99_CR15","unstructured":"Kennedy, J. (2003). Bare bones particle swarms. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium (pp. 80\u201387). Piscataway, NJ: IEEE."},{"key":"99_CR16","unstructured":"Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE International Joint Conference on Neural Networks (pp. 1942\u20131948). Piscataway, NJ: IEEE."},{"issue":"5","key":"99_CR17","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1109\/TEVC.2006.886448","volume":"11","author":"WB Langdon","year":"2007","unstructured":"Langdon, W. B., & Poli, R. (2007). Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Transactions on Evolutionary Computation, 11(5), 561\u2013578.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"99_CR18","unstructured":"Lu, G., Li, J., & Yao, X. (2011). Fitness-probability cloud and a measure of problem hardness for evolutionary algorithms. In P. Merz & J.-K. Hao (Eds.), Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science (Vol. 6622, pp. 108\u2013117). Berlin: Springer."},{"key":"99_CR19","unstructured":"Malan, K. M. (2014). Characterising Continuous Optimisation Problems for Particle Swarm Optimisation Performance Prediction. Ph.D. thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa."},{"key":"99_CR20","unstructured":"Malan, K. M., & Engelbrecht, A. P. (2013). Ruggedness, funnels and gradients in fitness landscapes and the effect on PSO performance. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 963\u2013970). Piscataway, NJ: IEEE."},{"key":"99_CR21","doi-asserted-by":"crossref","unstructured":"Malan, K. M., & Engelbrecht, A. P. (2013b). Steep gradients as a predictor of PSO failure. In Proceedings of the Fifteenth International Conference on Genetic and Evolutionary Computation Conference, Companion (pp. 9\u201310). New York, NY: ACM.","DOI":"10.1145\/2464576.2464582"},{"key":"99_CR22","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.ins.2013.04.015","volume":"241","author":"KM Malan","year":"2013","unstructured":"Malan, K. M., & Engelbrecht, A. P. (2013c). A survey of techniques for characterising fitness landscapes and some possible ways forward. Information Sciences, 241, 148\u2013163.","journal-title":"Information Sciences"},{"key":"99_CR23","doi-asserted-by":"crossref","unstructured":"Malan, K. M., & Engelbrecht, A. P. (2014a). Fitness landscape analysis for metaheuristic performance prediction. In H. Richter & A. P. Engelbrecht (Eds.), Recent Advances in the Theory and Application of Fitness Landscapes, Emergence, Complexity and Computation (Vol. 6, pp. 103\u2013132). Springer: Berlin.","DOI":"10.1007\/978-3-642-41888-4_4"},{"key":"99_CR24","doi-asserted-by":"crossref","unstructured":"Malan, K. M., & Engelbrecht, A. P. (2014b). Particle swarm optimisation failure prediction based on fitness landscape characteristics. In: Proceedings of IEEE Symposium on Swarm Intelligence. Piscataway, NJ: IEEE (to appear).","DOI":"10.1109\/SIS.2014.7011789"},{"key":"99_CR25","unstructured":"Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., & Rudolph, G. (2011). Exploratory landscape analysis. In Proceedings of the Thirteenth Annual Conference on Genetic and Evolutionary Computation (pp. 829\u2013836). New York, NY: ACM."},{"key":"99_CR26","unstructured":"M\u00fcller, C. L., & Sbalzarini, I. F. (2011). Global characterization of the CEC 2005 fitness landscapes using fitness-distance analysis. In C. Di Chio, et al. (Eds.), Applications of Evolutionary Computation. Lecture Notes in Computer Science (Vol. 6624, pp. 294\u2013303). Berlin: Springer."},{"key":"99_CR27","unstructured":"Mu\u00f1oz, M. A., Kirley, M., & Halgamuge, S. K. (2012). A meta-learning prediction model of algorithm performance for continuous optimization problems. In C. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, & M. Pavone (Eds.), Proceedings of the Twelfth International Conference on Parallel Problem Solving from Nature: Part I. Berlin: Springer."},{"key":"99_CR28","unstructured":"Naudts, B., & Kallel, L. (1998). Some Facts About So Called GA-Hardness Measures. Technical Report 379, CMAP, Ecole Polytechnique, France."},{"issue":"1","key":"99_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/4235.843491","volume":"4","author":"B Naudts","year":"2000","unstructured":"Naudts, B., & Kallel, L. (2000). A comparison of predictive measures of problem difficulty in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 4(1), 1\u201315.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"99_CR30","unstructured":"Quick, R. J., Rayward-Smith, V. J., & Smith, G. D. (1998). Fitness distance correlation and ridge functions. In A. E. Eiben, T. B\u00e4ck, M. Schoenauer, & H.-P. Schwefel (Eds.), Proceedings of the Fifth International Conference on Parallel Problem Solving from Nature (pp. 77\u201386). Berlin: Springer."},{"key":"99_CR31","unstructured":"Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco, CA: Morgan Kaufmann."},{"key":"99_CR32","unstructured":"Reeves, C. R. (1999). Predictive measures for problem difficulty. In Proceedings of the 1999 Congress on Evolutionary Computation (pp. 736\u2013743), Piscataway, NJ: IEEE."},{"key":"99_CR33","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0065-2458(08)60520-3","volume":"15","author":"JR Rice","year":"1976","unstructured":"Rice, J. R. (1976). The algorithm selection problem. Advances in Computers, 15, 65\u2013118.","journal-title":"Advances in Computers"},{"issue":"1","key":"99_CR34","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1162\/evco.2006.14.1.119","volume":"14","author":"YW Shang","year":"2006","unstructured":"Shang, Y. W., & Qiu, Y. H. (2006). A note on the extended Rosenbrock function. Evolutionary Computation, 14(1), 119\u2013126.","journal-title":"Evolutionary Computation"},{"issue":"1","key":"99_CR35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/106365602317301754","volume":"10","author":"T Smith","year":"2002","unstructured":"Smith, T., Husbands, P., Layzell, P., & O\u2019Shea, M. (2002). Fitness landscapes and evolvability. Evolutionary Computation, 10(1), 1\u201334.","journal-title":"Evolutionary Computation"},{"key":"99_CR36","unstructured":"Smith-Miles, K. (2008). Towards insightful algorithm selection for optimisation using meta-learning concepts. In Proceedings of the IEEE Joint Conference on Neural Networks (pp. 4118\u20134124). Piscataway, NJ: IEEE."},{"key":"99_CR37","unstructured":"Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Chen, Y. P., Auger, A., et al. (2005). Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization, Technical Report. Nanyang Technological University, Singapore."},{"issue":"6","key":"99_CR38","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/S0020-0190(02)00447-7","volume":"85","author":"IC Trelea","year":"2003","unstructured":"Trelea, I. C. (2003). The particle swarm optimization algorithm: Convergence analysis and parameter selection. Information Processing Letters, 85(6), 317\u2013325.","journal-title":"Information Processing Letters"},{"key":"99_CR39","unstructured":"Turney, P. D. (1999). Increasing evolvability considered as a large-scale trend in evolution. Proceedings of 1999 Genetic and Evolutionary Computation Conference Workshop Program (pp. 43\u201346). San Mateo, CA: Morgan Kaufmann."},{"issue":"8","key":"99_CR40","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.ins.2005.02.003","volume":"176","author":"F Bergh Van Den","year":"2006","unstructured":"Van Den Bergh, F., & Engelbrecht, A. P. (2006). A study of particle swarm optimization particle trajectories. Information Sciences, 176(8), 937\u2013971.","journal-title":"Information Sciences"},{"key":"99_CR41","unstructured":"Vanneschi, L. (2004). Theory and Practice for Efficient Genetic Programming. Ph.D. thesis, Faculty of Sciences, University of Lausanne, Switzerland."},{"key":"99_CR42","unstructured":"Vanneschi, L., Clergue, M., Collard, P., Tomassini, M., & Verel, S. (2004). Fitness clouds and problem hardness in genetic programming. In K. Deb, R. Poli, W. Banzhaf, H. G. Beyer, E. Burke, P. Darwen, D. Dasgupta, D. Floreano, J. Foster, M. Harman, O. Holland, P. L. Lanzi, L. Spector, A. G. B. Tettamanzi, D. Thierens, & A. Tyrrell (Eds.), Proceedings of Genetic and Evolutionary Computation Conference. Lecture Notes in Computer Science (Vol. 3103, pp. 690\u2013701). Berlin: Springer."},{"key":"99_CR43","unstructured":"Verel, S., Collard, P., & Clergue, M. (2003). Where are bottlenecks in NK fitness landscapes? In Proceedings of the 2003 Congress on Evolutionary Computation (Vol. 1, pp. 273\u2013280). New York, NY: ACM."}],"container-title":["Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11721-014-0099-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11721-014-0099-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11721-014-0099-x","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T14:10:05Z","timestamp":1565964605000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11721-014-0099-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,10,28]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2014,12]]}},"alternative-id":["99"],"URL":"https:\/\/doi.org\/10.1007\/s11721-014-0099-x","relation":{},"ISSN":["1935-3812","1935-3820"],"issn-type":[{"value":"1935-3812","type":"print"},{"value":"1935-3820","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,10,28]]}}}