Natural selection methods for artificial bee colony with new versions of onlooker bee

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms simulate the foraging behavior of honey bees in their hive. ABC starts with a colony of artificial bees with sole aim of discovering the place of food sources with high nectar amount using the solution search equation in the employed bee and onlooker bee operators. However, the solution search equation is good in exploration and poor in exploitation. In this paper, the solution search equation of the onlooker bee is modified by using a value of the fittest food sources selected by a set of selection schemes inspired from the evolutionary algorithms. This is to guide the search process of onlooker bee toward the fittest food sources from the population in order to empower the exploitation capability and convergence. Four selection schemes are incorporated with the ABC algorithm to choose the fittest food sources in four versions as follows: global-best, tournament, linear rank, and exponential rank. For evaluation purposes, 10 classical benchmark optimization functions are used to study the sensitivity analysis of each ABC algorithm to its parameters. The performance of the proposed ABC versions is compared with the original ABC version in order to study the effectiveness of the modifications. In addition, a comparative evaluation of ABC algorithms is carried out against the state-of-the-art methods that worked on CEC2005 benchmark functions, CEC2015 benchmark functions, and two real-world cases of economic load dispatch problem. The experimental results show that the selection schemes incorporated within the search equation of the onlooker bee directly affects the performance of ABC algorithm.

[1]  Jeffrey H. Kingston,et al.  An XML format for benchmarks in High School Timetabling , 2010, Ann. Oper. Res..

[2]  Mohammed Azmi Al-Betar,et al.  β\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}-Hill climbing: an exploratory local search , 2016, Neural Computing and Applications.

[3]  Lixiang Li,et al.  A hybrid CPSO–SQP method for economic dispatch considering the valve-point effects , 2012 .

[4]  Jan K. Sykulski,et al.  A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems , 2010 .

[5]  Bijaya K. Panigrahi,et al.  Neighborhood Search-Driven Accelerated Biogeography-Based Optimization for Optimal Load Dispatch , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Whei-Min Lin,et al.  Combining of Direct Search and Signal-to-Noise Ratio for economic dispatch optimization , 2011 .

[7]  István Erlich,et al.  Testing MVMO on learning-based real-parameter single objective benchmark optimization problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[8]  Mohammed Azmi Al-Betar,et al.  An Improved Artificial Bee Colony for Course Timetabling , 2011, 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications.

[9]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[10]  Abhinav Sadu,et al.  A hybrid multi-agent based particle swarm optimization algorithm for economic power dispatch , 2011 .

[11]  Barry McCollum,et al.  The Third International Timetabling Competition , 2012, Ann. Oper. Res..

[12]  Mohammed Azmi Al-Betar,et al.  Island-based harmony search for optimization problems , 2015, Expert Syst. Appl..

[13]  Mohammed Azmi Al-Betar,et al.  A hybrid artificial bee colony for a nurse rostering problem , 2015, Appl. Soft Comput..

[14]  Mohammed Azmi Al-Betar,et al.  Artificial bee colony algorithm, its variants and applications: A survey. , 2013 .

[15]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[16]  Mohammed A. Awadallah,et al.  Cellular Harmony Search for Optimization Problems , 2013, J. Appl. Math..

[17]  Quan-Ke Pan,et al.  A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion , 2015, Expert Syst. Appl..

[18]  Ya Li,et al.  Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model , 2014, Eng. Appl. Artif. Intell..

[19]  Lixiang Li,et al.  A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects , 2012 .

[20]  Whei-Min Lin,et al.  A novel stochastic search method for the solution of economic dispatch problems with non-convex fuel cost functions , 2011 .

[21]  Taher Niknam,et al.  A new honey bee mating optimization algorithm for non-smooth economic dispatch , 2011 .

[22]  Thomas Bäck,et al.  Selective Pressure in Evolutionary Algorithms: A Characterization of Selection Mechanisms , 1994, International Conference on Evolutionary Computation.

[23]  Mohammed Azmi Al-Betar,et al.  Tournament-based harmony search algorithm for non-convex economic load dispatch problem , 2016, Appl. Soft Comput..

[24]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search , 2005, 2005 IEEE Congress on Evolutionary Computation.

[25]  P. Subbaraj,et al.  Enhancement of Self-adaptive real-coded genetic algorithm using Taguchi method for Economic dispatch problem , 2011, Appl. Soft Comput..

[26]  Petr Posík,et al.  Real-parameter optimization using the mutation step co-evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[27]  Lingling Huang,et al.  A global best artificial bee colony algorithm for global optimization , 2012, J. Comput. Appl. Math..

[28]  Kalyanmoy Deb,et al.  A population-based, steady-state procedure for real-parameter optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[29]  P. Subbaraj,et al.  Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem , 2010 .

[30]  M. E. El-Hawary,et al.  Overview of Artificial Bee Colony (ABC) algorithm and its applications , 2012, 2012 IEEE International Systems Conference SysCon 2012.

[31]  Bijaya K. Panigrahi,et al.  Economic load dispatch solution by improved harmony search with wavelet mutation , 2011, Int. J. Comput. Sci. Eng..

[32]  Adnan Acan,et al.  Probability collectives hybridised with differential evolution for global optimisation , 2016, Int. J. Bio Inspired Comput..

[33]  Amir Hossein Alavi,et al.  Krill herd: A new bio-inspired optimization algorithm , 2012 .

[34]  Abbas Rabiee,et al.  Continuous quick group search optimizer for solving non-convex economic dispatch problems , 2012 .

[35]  Samia Nefti-Meziani,et al.  A Comprehensive Review of Swarm Optimization Algorithms , 2015, PloS one.

[36]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[37]  Robert G. Reynolds,et al.  A differential evolution algorithm with success-based parameter adaptation for CEC2015 learning-based optimization , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[38]  Mohammed Azmi Al-Betar,et al.  University course timetabling using hybridized artificial bee colony with hill climbing optimizer , 2014, J. Comput. Sci..

[39]  Leandro dos Santos Coelho,et al.  An improved harmony search algorithm for power economic load dispatch , 2009 .

[40]  Yu-Jun Zheng,et al.  Tuning maturity model of ecogeography-based optimization on CEC 2015 single-objective optimization test problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[41]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[42]  Yuren Zhou,et al.  A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization , 2015, Appl. Soft Comput..

[43]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[44]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[45]  L. Guo,et al.  A self-adaptive dynamic particle swarm optimizer , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[46]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[47]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[48]  Ruhul A. Sarker,et al.  Neurodynamic differential evolution algorithm and solving CEC2015 competition problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[49]  Xingsheng Gu,et al.  An improved discrete artificial bee colony algorithm to minimize the makespan on hybrid flow shop problems , 2015, Neurocomputing.

[50]  Abdullah Al-Dujaili,et al.  HumanCog: A cognitive architecture for solving optimization problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[51]  Ling Wang,et al.  An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems , 2013 .

[52]  Jason Sheng-Hong Tsai,et al.  A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[53]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[54]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[55]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[56]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[57]  G. Sheblé,et al.  Genetic algorithm solution of economic dispatch with valve point loading , 1993 .

[58]  Tomonobu Senjyu,et al.  Solving economic load dispatch problem with valve-point effects using a hybrid quantum mechanics inspired particle swarm optimisation , 2011 .

[59]  Ying Tan,et al.  Dynamic search fireworks algorithm with covariance mutation for solving the CEC 2015 learning based competition problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[60]  Mohammed El-Abd.  Hybrid cooperative co-evolution for the CEC15 benchmarks , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[61]  Taher Niknam,et al.  Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method , 2012 .

[62]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[63]  Eric S. Fraga,et al.  On the modelling of valve point loadings for power electricity dispatch , 2012 .

[64]  Adnan Acan,et al.  A two-stage memory powered Great Deluge algorithm for global optimization , 2014, Soft Computing.

[65]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[66]  Petr Bujok,et al.  Cooperation of optimization algorithms: A simple hierarchical model , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[67]  Marcus Gallagher,et al.  Experimental results for the special session on real-parameter optimization at CEC 2005: a simple, continuous EDA , 2005, 2005 IEEE Congress on Evolutionary Computation.

[68]  Samir Sayah,et al.  A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems , 2013, Appl. Soft Comput..

[69]  Weifeng Gao,et al.  A modified artificial bee colony algorithm , 2012, Comput. Oper. Res..

[70]  Mohammed Azmi Al-Betar,et al.  A Hybrid Nature-Inspired Artificial Bee Colony Algorithm for Uncapacitated Examination Timetabling Problems , 2015, J. Intell. Syst..

[71]  Abbas Rabiee,et al.  Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems , 2012 .

[72]  Sishaj P. Simon,et al.  Artificial Bee Colony Algorithm for Economic Load Dispatch Problem with Non-smooth Cost Functions , 2010 .

[73]  Zong Woo Geem,et al.  An analysis of selection methods in memory consideration for harmony search , 2013, Appl. Math. Comput..

[74]  Saku Kukkonen,et al.  Real-parameter optimization with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[75]  Mohammed A. Awadallah,et al.  Novel selection schemes for harmony search , 2012, Appl. Math. Comput..

[76]  Pedro J. Ballester,et al.  Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX , 2005, 2005 IEEE Congress on Evolutionary Computation.

[77]  Adnan Acan,et al.  A tournament-based competitive-cooperative multiagent architecture for real parameter optimization , 2015, Soft Computing.

[78]  Dervis Karaboga,et al.  On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation , 2015, Inf. Sci..

[79]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[80]  Mohammed Azmi Al-Betar,et al.  Economic load dispatch problems with valve-point loading using natural updated harmony search , 2018, Neural Computing and Applications.

[81]  Carlos García-Martínez,et al.  Hybrid real-coded genetic algorithms with female and male differentiation , 2005, 2005 IEEE Congress on Evolutionary Computation.

[82]  Leandro dos Santos Coelho,et al.  An efficient cultural self-organizing migrating strategy for economic dispatch optimization with valve-point effect , 2010 .

[83]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[84]  P. K. Chattopadhyay,et al.  Hybrid Differential Evolution With Biogeography-Based Optimization for Solution of Economic Load Dispatch , 2010, IEEE Transactions on Power Systems.

[85]  Thomas Stützle,et al.  A configurable generalized artificial bee colony algorithm with local search strategies , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[86]  W. H. Ip,et al.  Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem , 2014 .

[87]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[88]  N. Gupta,et al.  The Bisection-Artificial Bee Colony algorithm to solve Fixed point problems , 2015, Appl. Soft Comput..

[89]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[90]  Xiaofeng Zhang,et al.  Optimization and Parameters Estimation in Ultrasonic Echo Problems Using Modified Artificial Bee Colony Algorithm , 2015 .

[91]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[92]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[93]  Z. Dong,et al.  Quantum-Inspired Particle Swarm Optimization for Valve-Point Economic Load Dispatch , 2010, IEEE Transactions on Power Systems.

[94]  P. K. Chattopadhyay,et al.  Solving complex economic load dispatch problems using biogeography-based optimization , 2010, Expert Syst. Appl..

[95]  Nima Amjady,et al.  Solution of non-convex economic dispatch problem considering valve loading effect by a new Modified Differential Evolution algorithm , 2010 .

[96]  Waree Kongprawechnon,et al.  Ant colony optimisation for economic dispatch problem with non-smooth cost functions , 2010 .

[97]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[98]  Debasish Ghose,et al.  Detection of multiple source locations using a glowworm metaphor with applications to collective robotics , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[99]  Francisco Herrera,et al.  Adaptive local search parameters for real-coded memetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[100]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[101]  Peter J. B. Hancock,et al.  An Empirical Comparison of Selection Methods in Evolutionary Algorithms , 1994, Evolutionary Computing, AISB Workshop.

[102]  Janne Heikkilä,et al.  Predicting the Valence of a Scene from Observers’ Eye Movements , 2015, PloS one.

[103]  Tharam S. Dillon,et al.  Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function , 2010 .

[104]  Marco Dorigo.  Ant colony optimization , 2004, Scholarpedia.