Abstract
This paper proposes a novel quasi-oppositional chaotic antlion optimizer (ALO) (QOCALO) for solving global optimization problems. ALO is a population based algorithm motivated by the unique hunting behavior of antlions in nature and exhibits strong influence in solving global and engineering optimization problems. In the proposed QOCALO algorithm of the present work, the initial population is generated using the quasi-opposition based learning (QOBL) and the concept of QOBL based generation jumping is utilized inside the main searching strategy of the proposed algorithm. Utilization of QOBL ensures better convergence speed of the proposed algorithm and it also provides better exploration of the search space. Alongside the QOBL, a chaotic local search (CLS) is also incorporated in the proposed QOCALO algorithm. The CLS guides local search around the global best solution that provides better exploitation of the search space. Thus, a better trade-off between exploration and exploitation holds for the proposed algorithm which makes it robust. It is observed that the proposed algorithm offers better results than the original ALO in terms of solution quality and convergence speed. The proposed QOCALO algorithm is implemented and tested, successfully, on nineteen mathematical benchmark test functions of varying complexities and the experimental results are compared to those offered by the basic ALO and some other recently developed nature inspired algorithms. The efficacy of the proposed algorithm is further utilized to solve three real world engineering optimization problems viz. (a) the placement and sizing problem of distributed generators in radial distribution networks, (b) the congestion management problem in power transmission system and (c) the optimal design of pressure vessel.
Similar content being viewed by others
References
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Boston
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. Perth, pp 1942–1948
Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471
Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1:28–39
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82
Yang XS (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and applications. SAGA 2009. Lecture notes in computer science, vol 5792. Springer, Berlin, pp 169–178
Kashan AH (2014) League championship algorithm (LCA): an algorithm for global optimization inspired by sport championships. Appl Soft Comput 16:171–200
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: A gravitational search algorithm. Inf Sci 179:2232–2248
Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Gonzalez J R et al (eds) Nature inspired cooperative strategies for optimization. NISCO 2010. Studies in computational intelligence, vol 284. Springer, Berlin, pp 65–74
Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35
Rao RV, Savsani VJ, Balic J (2012) Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng Optim 44:1447–1462
Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17:4831–4845
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2012) Mine blast algorithm for optimization of truss structures with discrete variables. Comput Struct 102-103:49–63
Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
Zheng YJ (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowledge-Based Syst 89:228–249
Ebrahimi A, Khamehchi E (2016) Sperm whale algorithm: an effective metaheuristic algorithm for production optimization problems. J Nat Gas Sci Eng 29:211–222
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12
Gang M, Wei Z, Xiaolin C (2012) A novel particle swarm optimization algorithm based on particle migration. Appl Math Comput 218:6620–6626
Zou F, Wang L, Hei X, Chen D, Yang D (2014) Teaching-learning-based optimization with dynamic group strategy for global optimization. Inf Sci 273:112–131
Guo L, Wang GG, Gandomi AH, Alavi AH, Duan H (2014) A new improved krill herd algorithm for global numerical optimization. Neurocomputing 138:392–402
Jordehi AR (2015) Enhanced leader PSO (ELPSO): a new PSO variant for solving global optimisation problems. Appl Soft Comput 26:401–417
Ghasemi M, Ghavidel S, Gitizadeh M, Akbari E (2015) An improved teaching-learning-based optimization algorithm using lévy mutation strategy for non-smooth optimal power flow. Int J Electr Power Energy Syst 65:375–384
Kiran MS, Hakli H, Gunduz M, Uguz H (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf Sci 300:140–157
Mirjalili S, Hashim SZM (2010) A new hybrid PSOGSA algorithm for function optimization. In: International conference on computer and information application. Tianjin, pp 374–377
Huang J, Gao L, Li X (2015) An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Appl Soft Comput 36:349–356
Li Z, Nguyen TT, Chen S, Truong TK (2015) A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems. Appl Soft Comput 35:525–540
Mahi M, Baykan OK, Kodaz H (2015) A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem. Appl Soft Comput 30:484–490
Dubey HM, Pandit M, Panigrahi BK (2016) Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling. Int J Electr Power Energy Syst 83:158–174
Dubey HM, Pandit M, Panigrahi BK (2016) Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index. Renew Energy 99:18–34
Raju M, Saikia LC, Sinha N (2016) Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller. Int J Electr Power Energy Syst 80:52–63
Nair SS, Rana KPS, Kumar V, Chawla A (2017) Efficient modeling of linear discrete filters using ant lion optimizer. Circuits Syst Signal Process 36:1535–1568
Tizhoosh HR (2006) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modeling, control and automation 2005 and international conference on intelligent agents. Web technologies and internet commerce. Vienna, pp 695–701
Tizhoosh HR (2005) Reinforcement learning based on actions and opposite actions. In: ICGST conference on artificial intelligence and machine learning. Cairo, pp 94–98
Rahnamayan S, Tizhoosh HR, Salama MMA (2007) Quasi-oppositional differential evolution. In: Proceedings of the IEEE congress on evolutionary computation (CEC’2007). Singapore, pp 2229–2236
Shiva CK, Shankar G, Mukherjee V (2015) Automatic generation control of power system using a novel quasi-oppositional harmony search algorithm. Int J Electr Power Energy Syst 73:787–804
Sultana S, Roy PK (2014) Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. Int J Electr Power Energy Syst 63:534–545
Basu M (2016) Quasi-oppositional group search optimization for multi-area dynamic economic dispatch. Int J Electr Power Energy Syst 78:356–367
Liu B, Wang L, Jin YH, Tang F, Huang DX (2005) Improved particle swarm optimization combined with chaos. Chaos, Solitons Fractals 25:1261–1271
Xiang T, Liao X, Wong K (2007) An improved particle swarm optimization algorithm combined with piecewise linear chaotic map. Appl Math Comput 190:1637–1645
Hefny HA, Azab SS (2010) Chaotic particle swarm optimization. In: International conference on informatics and systems. Cairo, pp 1–8
Xia X (2012) Particle swarm optimization method based on chaotic local search and roulette wheel mechanism. Phys Procedia 24:269–275
Xu W, Geng Z, Zhu Q, Gu X (2013) A piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization. Inf Sci 218:85–102
Turgut OE (2016) Hybrid chaotic quantum behaved particle swarm optimization algorithm for thermal design of plate fin heat exchangers. Appl Math Model 40:50–69
Li P, Xu D, Zhou Z, Lee W, Zhao B (2016) Stochastic optimal operation of microgrid based on chaotic binary particle swarm optimization. IEEE Trans Smart Grid 7:66–73
Bharti KK, Singh PK (2016) Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering. Appl Soft Comput 43:20–34
Jia D, Zheng G, Khan MK (2011) An effective memetic differential evolution algorithm based on chaotic local search. Inf Sci 181:3175–3187
He Y, Xu Q, Yang S, Han A, Yang L (2014) A novel chaotic differential evolution algorithm for short-term cascaded hydroelectric system scheduling. Int J Electr Power Energy Syst 61:455–462
Zhang J, Lin S, Qiu W (2015) A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling. Int J Electr Power Energy Syst 65:159–168
Lu P, Zhou J, Zhang H, Zhang R, Wang C (2014) Chaotic differential bee colony optimization algorithm for dynamic economic dispatch problem with valve-point effects. Int J Electr Power Energy Syst 62:130–143
Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37:5682–5687
Bharti KK, Singh PK (2016) Chaotic gradient artificial bee colony for text clustering. Soft Comput 20:1113–1126
Pan QK, Wang L, Gao L (2011) A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers. Appl Soft Comput 11:5270–5280
He X, Rao Y, Huang J (2016) A novel algorithm for economic load dispatch of power systems. Neurocomputing 171:1454–1461
Saha S, Mukherjee V (2016) Optimal placement and sizing of DGs in RDS using chaos embedded SOS algorithm. IET Gener Transm Distrib 10:3671–3680
Saha S, Mukherjee V (2017) A novel chaos-integrated symbiotic organisms search algorithm for global optimization. Soft Comput. https://doi.org/10.1007/s00500-017-2597-4
Kapitaniak T (1995) Continuous control and synchronization in chaotic systems. Chaos, Solitons Fractals 6:237–244
Tavazoei MS, Haeri M (2007) Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Appl Math Comput 187:1076–1085
Alatas B, Akin E, Ozer AB (2009) Chaos embedded particle swarm optimization algorithms. Chaos, Solitons Fractals 40:1715–1734
Popović DH, Greatbanks JA, Begović M, Pregelj A (2005) Placement of distributed generators and reclosers for distribution network security and reliability. Int J Electr Power Energy Syst 27:398–408
Borges CLT, Falcão DM (2006) Optimal distributed generation allocation for reliability, losses, and voltage improvement. Int J Electr Power Energy Syst 28:413–420
Singh D, Singh D, Verma KS (2009) Multiobjective optimization for DG planning with load models. IEEE Trans Power Syst 24:427–436
Nafar M (2012) PSO Based optimal placement of DGs in distribution systems considering voltage stability and short circuit level improvement. J Basic Appl Sci Res 2:703–709
Ganguly S, Sahoo NH, Das D (2013) Multi-objective particle swarm optimization based on fuzzy-pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation. Fuzzy Sets Syst 213:47–73
Nayak MR, Dash SK, Rout PK (2012) Optimal placement and sizing of distributed generation in radial distribution system using differential evolution algorithm. In: Panigrahi et al (eds) Swarm evolutionary, and memetic computing. SEMCCO 2012. Lecture notes in computer science. Springer, Berlin, pp 133–142
Falaghi H, Haghifam MR (2007) ACO based algorithm for distributed generation sources allocation and sizing in distribution systems. In: Proceedings of IEEE Lausanne POWERTECH. Lausanne, pp 555–560
Abu-Mouti FS, El-Hawary ME (2011) Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. IEEE Trans Power Deliv 26:2090–2101
Moradi MH, Abedini M (2012) A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int J Electr Power Energy Syst 34:66–74
Mohandas N, Balamurugan R, Lakshminarasimman L (2015) Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system using ABC algorithm united with chaos. Int J Electr Power Energy Syst 66:41–52
Chakravorty M, Das D (2001) Voltage stability analysis of radial distribution networks. Int J Electr Power Energy Syst 23:129–135
Verma S, Mukherjee V (2016) Optimal real power rescheduling of generators for congestion management using a novel ant lion optimizer. IET Gener Transm Distrib 10:2548–2561
Balaraman S, Kamaraj N (2011) Transmission congestion management using particle swarm optimization. J Electr Syst 7:54–70
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99
Coello Coello CA, Mezura Montes E (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16:193–203
Li L, Huang Z, Liu F, Wu Q (2007) A heuristic particle swarm optimizer for optimization of pin connected structures. Comput Struct 85:340–349
Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27:155–182
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
The detailed mathematical formulation of the Weierstrass function is provided in Table 20.
Rights and permissions
About this article
Cite this article
Saha, S., Mukherjee, V. A novel quasi-oppositional chaotic antlion optimizer for global optimization. Appl Intell 48, 2628–2660 (2018). https://doi.org/10.1007/s10489-017-1097-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-017-1097-7