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The Multi-Swarm approach allows the use of multiple configurations between two or more populations of particles, where each one can present different approaches (e.g. lbest, gbest, Unified, Guaranteed-Convergence) directed towards improving the optimization process. This article presents a proposal for local/global stochastic interconnection applied to the context of the Multi-Swarm algorithm, as well as for incrementing a local search method for refining previously obtained solutions. Two proposals are introduced for this new Multi-Swarm PSO (MSO). The first one is the inclusion of "counterpart particles", which establishes a sub-topology between inter-swarm particles, accessed by migration and evaluability rules. The other involves using customized crossover operators and is based on the BLX scheme (Blend Crossover) with direction information used as a reference for establish a subspace search around the particles. Performance and robustness of the new approaches were assessed by ten constrained engineering design optimization problems (COPs), as is compared to other solutions already published in the scientific literature. Results indicate significant performance improvements for all 10 COPs when compared to concurrent-based MSOs. By making available new references from other swarms, the counterpart particles approach tends to improve the optimization process in the search space, while an intermediate layer of local search based on a modified directed BLX crossover should provide an extra search around the particle, and thus, refining previously obtained solutions.

SummaryThis paper presents a modified genetic algorithm (GA) using a new crossover operator (ADX) and a novel statistic correlation mutation algorithm (CAM). Both ADX and CAM work with population information to improve existing individuals of the GA and increase the exploration potential via the correlation mutation. Solution-based methods offer better local improvement of already known solutions while lacking at exploring the whole search space; in contrast, evolutionary algorithms provide better global search in exchange of exploitation power. Hybrid methods are widely used for constrained optimization problems due to increased global and local search capabilities. The modified GA improves results of constrained problems by balancing the exploitation and exploration potential of the algorithm. The conducted tests present average performance for various CEC’2015 benchmark problems, while offering better reliability and superior results on path planning problem for redundant manipulator and most of the constrained engineering design problems tested compared with current works in the literature and classic optimization algorithms.

The evolutionary process of a Genetic Algorithm (GA) depends on several factors, the initialization parameters are some of them [4]. One way to inspect the evolutionary process of a GA is to analyze the maximum, average, and minimum fitness of each generation. This article focused on the use of a machine learning model to predict the maximum, average, and minimum fitness values during the evolutionary process of a GA, and this, only with the knowledge of its initialization parameters. In order to accomplish this goal, a Random Forest model was trained with data from different GA executions for a given problem. The prediction process was performed with a very promising performance, where the challenge of predicting the evolutionary process of a GA was fulfilled with low error rates. This approach opens up several opportunities for advances in the segment, and in a way, contributes to the investigation and improvement of GAs, as well as demonstrating the importance of monitoring and storing the information generated during their evolutionary process.

This article presents a parameter study on the applied game theory in Genetic Algorithm (GA), performing an analysis of the game Prisoner's Dilemma applied in the solution of four constrained Engineering problems. Simulations were applied in four different variations of the game, in order to find the best configuration for the analyzed problems. Then, after obtaining the best configuration of the game, we performed an analysis of the incidence of Alpha and Beta weights, present in GA fitness with Social Interaction, using the game that best suited each problem, employing sixteen different weight configurations. At end, the results obtained authenticated the influence of the Social Interaction process in the essence of GA.

The following paper presents a hybrid Swarm Intelligence algorithm, named Competitive Evolutionary Multi-Colony Optimization (CEMCO). CEMCO is based on the well-known bee inspired algorithm Artificial Bee Colony (ABC), introducing Evolutionary Strategies (ES)under a parallel master-slave architecture utilizing the General Purpose Computing on Graphics Processing Units (GPGPU)paradigm. The goal of this approach is to enhance the overall performance of the master algorithm, producing high quality solutions, and significant solutions under a reduced processing time. Performance evaluation of CEMCO is performed by two large scale constrained optimization problems: Design of Pressure Vessel (DPV)and Welded Beam Design (WBD). Results obtained are compared against prominent techniques found in the literature.

This paper describes the development of a new hybrid meta-heuristic of optimization based on a viral lifecycle, specifically the retroviruses (the nature's swiftest evolvers'), called Retroviral Iterative Genetic Algorithm (RIGA). This algorithm uses Genetics Algorithms (GA) structures with features of retroviral replication, providing a great genetic diversity, confirmed by better results achieved by RIGA comparing with GA applied to some Real-Valued Benchmarking Functions.

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.

In this paper, in order to prove the effectiveness of the Imperialist Competitive Algorithm — a socio-political inspired algorithm-on finding the optimal solution for different kinds of minimization functions as well as different kinds of landscapes. The reliability and quality of solutions for mathematical minimization functions of the ICA is evaluated by seven distinct benchmark functions where each one displays different behaviors, and then the results of each test is compared with two other optimization techniques, the Particle Swarm Optimization (PSO) and the Differential Evolution (DE).

Evolutionary Computation (EC) is inspired from by evolution that explores the solution space by gene inheritance, mutation, and selection of the fittest candidate solutions. Since their inception in the 1960s, Evolutionary Computation has been used in various hard and complex optimization problems in search and optimization such as: combinatorial optimization, functions optimization with and without constraints, engineering problems and others (Adeyemo, 2011). This success is in part due to the unbiased nature of their operations, which can still perform well in situations with little or no domain knowledge (Reynolds, 1999). The basic EC framework consists of fairly simple steps like definition of encoding scheme, population generation method, objective function, selection strategy, crossover and mutation (Ahmed & Younas, 2011). In addition, the same procedures utilized by EC can be applied to diverse problems with relatively little reprogramming.

This paper describes the development of a new hybrid meta-heuristic of optimization based on a viral lifecycle, specifically the retroviruses (the nature's swiftest evolvers'), called Retroviral Iterative Genetic Algorithm (RIGA). This algorithm uses Genetics Algorithms (GA) structures with features of retroviral replication, providing a great genetic diversity, confirmed by better results achieved by RIGA comparing with GA applied to some Real-Valued Benchmarking Functions.

In this paper, we present a Fuzzy Influence Function for the CAEP model (Cultural Algorithms with Evolutionary Programming) proposed by Chung [1] and extended by Zhu [6], applied to real-valued function optimization. The proposal makes use of a Fuzzy Inference System 'FIS' to adjust an Influence Factor that represents the intensity of the influence of the Variation operator of the CAEP model. This paper also presents a comparative analysis of the proposed influence function using a set of 17 of the CEC '05 benchmarking functions.