Simplified tabu search with random-based searches for bound constrained global optimization

This paper proposes a simplified version of the tabu search algorithm that solely uses randomly generated direction vectors in the exploration and intensification search procedures, in order to define a set of trial points while searching in the neighborhood of a given point. In the diversification procedure, points that are inside any already visited region with a relative small visited frequency may be accepted, apart from those that are outside the visited regions. The produced numerical results show the robustness of the proposed method. Its efficiency when compared to other known metaheuristics available in the literature is encouraging.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00013/2020); FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, UIDB/00013/2020 and UIDP/00013/2020 of CMAT-UM

Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism

Firefly algorithm (FA) is a new swarm intelligence optimization algorithm, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence when solving complex optimization problems. In this paper, we propose a new FA variant, called NSRaFA, which employs a random attraction model and three neighborhood search strategies to obtain a trade-off between exploration and exploitation abilities. Moreover, a dynamic parameter adjustment mechanism is used to automatically adjust the control parameters. Experiments are conducted on a set of well-known benchmark functions. Results show that our approach achieves much better solutions than the standard FA and five other recently proposed FA variants

Image-based benchmarking and visualization for large-scale global optimization

In the context of optimization, visualization techniques can be useful for understanding the behaviour of optimization algorithms and can even provide a means to facilitate human interaction with an optimizer. Towards this goal, an image-based visualization framework, without dimension reduction, that visualizes the solutions to large-scale global optimization problems as images is proposed. In the proposed framework, the pixels visualize decision variables while the entire image represents the overall solution quality. This framework affords a number of benefits over existing visualization techniques including enhanced scalability (in terms of the number of decision variables), facilitation of standard image processing techniques, providing nearly infinite benchmark cases, and explicit alignment with human perception. To the best of the authors’ knowledge, this is the first realization of a dimension-preserving, scalable visualization framework that embeds the inherent relationship between decision space and objective space. The proposed framework is utilized with different mapping schemes on an image-reconstruction problem that encompass continuous, discrete, constrained, dynamic, and multi-objective optimization. The proposed framework is then demonstrated on arbitrary benchmark problems with known optima. Experimental results elucidate the flexibility and demonstrate how valuable information about the search process can be gathered via the proposed visualization framework. Results of a user survey strongly support that users perceive a correlation between objective fitness values and the quality of the corresponding images generated by the proposed framework

Using semi-independent variables to enhance optimization search

In this study, the concept of a semi-independent variable (SIV) problem representation is investigated that embodies a set of expected or desired relationships among the original variables, with the goal of increasing search effectiveness and efficiency. The proposed approach intends to eliminate the generation of infeasible solutions associated with the known relationships among the variables and cutting the search space, thereby potentially improving a search algorithm's convergence rate and narrowing down the search space. However, this advantage does not come for free. The issue is the multiplicity of SIV formulations and their varying degree of complexity, especially with respect to variable interaction. In this paper, we propose the use of automatic variable interaction analysis methods to compare and contrast different SIV formulations. The performance of the proposed approach is demonstrated by implementing it within a number of classical and evolutionary optimization algorithms (namely, interior-point algorithm, simulated annealing, particle swarm optimization, genetic algorithm and differential evolution) in the application to several practical engineering problems. The case study results clearly show that the population-based algorithms can significantly benefit from the proposed SIV formulation resulting in better solutions with fewer function evaluations than in the original approach. The results also indicate that an automatic variable interaction analysis is capable of estimating the difficulty of the resultant SIV formulations prior to any optimization attempt

Using semi-independent variables to enhance optimization search

In this study, the concept of a semi-independent variable (SIV) problem representation is investigated that embodies a set of expected or desired relationships among the original variables, with the goal of increasing search effectiveness and efficiency. The proposed approach intends to eliminate the generation of infeasible solutions associated with the known relationships among the variables and cutting the search space, thereby potentially improving a search algorithm's convergence rate and narrowing down the search space. However, this advantage does not come for free. The issue is the multiplicity of SIV formulations and their varying degree of complexity, especially with respect to variable interaction. In this paper, we propose the use of automatic variable interaction analysis methods to compare and contrast different SIV formulations. The performance of the proposed approach is demonstrated by implementing it within a number of classical and evolutionary optimization algorithms (namely, interior-point algorithm, simulated annealing, particle swarm optimization, genetic algorithm and differential evolution) in the application to several practical engineering problems. The case study results clearly show that the population-based algorithms can significantly benefit from the proposed SIV formulation resulting in better solutions with fewer function evaluations than in the original approach. The results also indicate that an automatic variable interaction analysis is capable of estimating the difficulty of the resultant SIV formulations prior to any optimization attempt

Diversity Rate of Change Measurement for Particle Swarm Optimisers

The diversity of a particle swarm can reflect the swarm's explorative/exploitative behaviour at a given time step. This paper pro- poses a diversity rate of change measure to quantify the rate at which particle swarms decrease their diversity over time. The proposed measure is based on a two-piecewise linear approximation of diversity measurements sampled at regular time steps. The proposed measure is the slope of the first of the two lines. It is shown that, when comparing the measure among different algorithms, the measure reflects the differences in the behaviour of algorithms in terms of their exploration-exploitation trade-o . The measure can potentially be used to characterise and classify different algorithms based on algorithm behaviour.http://link.springer.combookseries/558hb201

Robust multi-user detection based on hybrid grey wolf optimization

The search for an effective nature-inspired optimization technique has certainly continued for decades. In this paper, a novel hybrid Grey wolf optimization and differential evolution algorithm robust multi-user detection algorithm is proposed to overcome the problem of high bit error rate (BER) in multi-user detection under impulse noise environment. The simulation results show that the iteration times of the multi-user detector based on the proposed algorithm is less than that of genetic algorithm, differential evolution algorithm and Grey wolf optimization algorithm, and has the lower BER