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The Artificial Bee Colony (ABC) is a well-known simple and efficient bee inspired metaheuristic that has been showed to achieve good performance on real valued optimization problems. Inspired by such, a Quick Artificial Bee Colony (QABC) was proposed by Karaboga to enhance the global search and bring better analogy to the dynamic of bees. To improve its local search capabilities, a modified version of it, called Evolutionary Quick Artificial Bee Colony (EQABC), is proposed. The novel algorithm employs the mutation operators found in Evolutionary Strategies (ES) that was applied in ABC from Evolutionary Particle Swarm Optimization (EPSO). In order to test the performance of the new algorithm, it was applied in four large-scale constrained optimization structural engineering problems. The results obtained by EQABC are compared to original ABC, QABC, and ABC + ES, one of the algorithms inspired for the development of EQABC.

This paper has the purpose to show game theory (GT) applied to genetic algorithms (GA) as a new type of interaction between individuals of GA. The game theory increases the exploration potential of the genetic algorithm by changing the fitness with social interaction between individuals, avoiding the algorithm to fall in a local optimum. To increase the exploitation potential of this approach, this work will present the adjusted crossover operator and compare results to other crossover methods.

The following paper introduces a hybrid algorithm that combines Artificial Bee Colony Algorithm (ABC) and a model of Evolution Strategies (ES) found in the Evolutionary Particle Swarm Optimization (EPSO), another hybrid metaheuristic. The goal of this approach is to incorporate the effectiveness and simplicity of the ABC with the thorough local search mechanism of the Evolution Strategies in order to devise an algorithm that is able to achieve better optimality in less time than the original ABC applied to function optimization problems. With the intention of assessing this novel algorithm performance and reliability, several unconstrained benchmark functions as well as four large-scale constrained optimization-engineering problems (WBD, DPV, SRD-11 and MWTCS) act as an evaluation environment. The results obtained by the ABC+ES are compared to original ABC and several other optimization techniques.

This paper presents a new bio-inspired algorithm named Competitive Quantum-Behaviour Evolutionary Multi-Swarm Optimization (CQEMSO) based on CUDA parallel architecture applied to solve engineering problems, using the concept of master/slave swarm working under a competitive scheme and being executed over the paradigm of General Purpose Computing on Graphics Processing Units (GPGPU). The efforts on implementing the CQEMSO algorithm are focused at generating a solution which includes greater quality of search and higher speed of convergence by using mechanisms of evolutionary strategies with the procedures of search and optimization found in the classic QPSO. For performance analysis, the proposed solution was submitted to some well-known engineering problems (WBD, DPV) and its results compared to other solutions found on scientific literature.