Multi-population inflationary differential evolution algorithm with adaptive local restart

In this paper a Multi-Population Inflationary Differential Evolution algorithm with Adaptive Local Restart is presented and extensively tested over more than fifty test functions from the CEC 2005, CEC 2011 and CEC 2014 competitions. The algorithm combines a multi-population adaptive Differential Evolution with local search and local and global restart procedures. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The local restart of the population, which follows the local search, is, therefore, automatically adapted

A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS) operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011

Linearized biogeography-based optimization with re-initialization and local search

Biogeography-based optimization (BBO) is an evolutionary optimization algorithm that uses migration to share information among candidate solutions. One limitation of BBO is that it changes only one independent variable at a time in each candidate solution. In this paper, a linearized version of BBO, called LBBO, is proposed to reduce rotational variance. The proposed method is combined with periodic re-initialization and local search operators to obtain an algorithm for global optimization in a continuous search space. Experiments have been conducted on 45 benchmarks from the 2005 and 2011 Congress on Evolutionary Computation, and LBBO performance is compared with the results published in those conferences. The results show that LBBO provides competitive performance with state-of-the-art evolutionary algorithms. In particular, LBBO performs particularly well for certain types of multimodal problems, including high-dimensional real-world problems. Also, LBBO is insensitive to whether or not the solution lies on the search domain boundary, in a wide or narrow basin, and within or outside the initialization domain

Improving Robustness in Social Fabric-based Cultural Algorithms

In this thesis, we propose two new approaches which aim at improving robustness in social fabric-based cultural algorithms. Robustness is one of the most significant issues when designing evolutionary algorithms. These algorithms should be capable of adapting themselves to various search landscapes. In the first proposed approach, we utilize the dynamics of social interactions in solving complex and multi-modal problems. In the literature of Cultural Algorithms, Social fabric has been suggested as a new method to use social phenomena to improve the search process of CAs. In this research, we introduce the Irregular Neighborhood Restructuring as a new adaptive method to allow individuals to rearrange their neighborhoods to avoid local optima or stagnation during the search process. In the second approach, we apply the concept of Confidence Interval from Inferential Statistics to improve the performance of knowledge sources in the Belief Space. This approach aims at improving the robustness and accuracy of the normative knowledge source. It is supposed to be more stable against sudden changes in the values of incoming solutions. The IEEE-CEC2015 benchmark optimization functions are used to evaluate our proposed methods against standard versions of CA and Social Fabric. IEEE-CEC2015 is a set of 15 multi-modal and hybrid functions which are used as a standard benchmark to evaluate optimization algorithms. We observed that both of the proposed approaches produce promising results on the majority of benchmark functions. Finally, we state that our proposed strategies enhance the robustness of the social fabric-based CAs against challenges such as multi-modality, copious local optima, and diverse landscapes