Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
On the Analysis of Performance of the Improved Artificial-Bee-Colony Algorithm
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
Structural inverse analysis by hybrid simplex artificial bee colony algorithms
Computers and Structures
Comparison and Analysis of the Selection Mechanism in the Artificial Bee Colony Algorithm
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Artificial Bee Colony Programming Made Faster
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 04
Parameter Tuning for the Artificial Bee Colony Algorithm
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Solving Integer Programming Problems by Using Artificial Bee Colony Algorithm
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
Chaotic bee colony algorithms for global numerical optimization
Expert Systems with Applications: An International Journal
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures
Applied Soft Computing
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
The best-so-far selection in Artificial Bee Colony algorithm
Applied Soft Computing
Artificial Bee Colony algorithm for optimization of truss structures
Applied Soft Computing
A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems
Applied Soft Computing
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Comparison of different mutation strategies applied to artificial bee colony algorithm
ECC'11 Proceedings of the 5th European conference on European computing conference
Guided artificial bee colony algorithm
ECC'11 Proceedings of the 5th European conference on European computing conference
A modified artificial bee colony algorithm
Computers and Operations Research
SAR image segmentation based on Artificial Bee Colony algorithm
Applied Soft Computing
Performance assessment of foraging algorithms vs. evolutionary algorithms
Information Sciences: an International Journal
DisABC: A new artificial bee colony algorithm for binary optimization
Applied Soft Computing
Engineering optimizations via nature-inspired virtual bee algorithms
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
A hybrid swarm intelligent method based on genetic algorithm and artificial bee colony
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Job Shop Scheduling with the Best-so-far ABC
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A clustering particle based artificial bee colony algorithm for dynamic environment
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing
Applied Soft Computing
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Evolutionary computation (EC) paradigm has undergone extensions in the recent years diverging from the natural process of genetic evolution to the simulation of natural life processes exhibited by the living organisms. Bee colonies exemplify a high level of intrinsic interdependence and co-ordination among its members, and algorithms inspired from the bee colonies have gained recent prominence in the field of swarm based metaheuristics. The artificial bee colony (ABC) algorithm was recently developed, by simulating the minimalistic foraging model of honeybees in search of food sources, for solving real-parameter, non-convex, and non-smooth optimization problems. The single parameter perturbation in classical ABC resulted in fairly commendable performance for simple problems without epistasis of variables (separable). However, it suffered from narrow search zone and slow convergence which eventually led to poor exploitation tendency. Even with the increase in dimensionality, a significant deterioration was observed in the ability of ABC to locate the optimum in a huge search volume. Some of the probable shortcomings in the basic ABC approach, as observed, are the single parameter perturbation instead of a multiple one, ignoring the fitness to reward ratio while selecting food sites, and most importantly the absence of environmental factors in the algorithm design. Research has shown that spatial environmental factors play a crucial role in insect locomotion and foragers seem to learn the direction to be undertaken based on the relative analysis of its proximal surroundings. Most importantly, the mapping of the forager locomotion from three dimensional search spaces to a multidimensional solution space calls forth the implementation of multiple modification schemes. Based on the fundamental observation pertaining to the dynamics of ABC, this article proposes an improved variant of ABC aimed at improving the optimizing ability of the algorithm over an extended set of problems. The hybridization of the proposed fitness learning mechanism with a weighted selection scheme and proximity based stimuli helps to achieve a fine blending of explorative and exploitative behaviour by enhancing both local and global searching ability of the algorithm. This enhances the ability of the swarm agents to detect optimal regions in the unexplored fitness basins. With respect to its immediate surroundings, a proximity based component is added to the normal positional modification of the onlookers and is enacted through an improved probability selection scheme that takes the T/E (total reward to distance) ratio metric into account. The biologically-motivated, hybridized variant of ABC achieves a statistically superior performance on majority of the tested benchmark instances, as compared to some of the most prominent state-of-the-art algorithms, as is demonstrated through a detailed experimental evaluation and verified statistically.