Search

Differential search (DSA) is a recently proposed evolutionary algorithm mimicking the Brownian motion-like random movement existing in living beings. Though it has displayed promise for global optimization, the original DSA suffers from relatively poor search capability, especially for exploitation. In this study, an augmented DSA (ADSA) is proposed by integrating memetic framework with multiple strategies. In ADSA, a sub-gradient strategy is combined to improve local exploitation, and the dynamic Levy flight technique is developed to strengthen the global exploration. Moreover, a mutation operator based on differential search is used to increase swarm diversity. An intelligent selection method is implemented to adaptively adjust the strategies based on historical performance. To fully benchmark the proposed algorithm, 35 test functions of various properties in 30-D and 100-D are adopted in numerical experiments. Seven canonical optimization algorithms are involved for experimental comparison. In addition, two real-world problems are also tested to verify ADSA’s practical applicability. Numerical results reveal the efficiency and effectiveness of ADSA.

Monarch butterfly optimization algorithm (MBO) has recently been proposed as a robust metaheuristic optimization algorithm for solving numerical global optimization problems. To enhance the performance of MBO algorithm, harmony search (HS) is introduced as a mutation operator during the adjusting operator of MBO. A novel hybrid metaheuristic optimization method, the so-called HMBO, is introduced to find the best solution for the global optimization problems. HMBO combines HS exploration with MBO exploitation, and therefore, it produces potential candidate solutions. The implementation process for enhancing MBO method is also presented. To evaluate the effectiveness of this improvement, fourteen standard benchmark functions are used. The mean and the best performance of these benchmark functions in 20, 50, and 100 dimensions demonstrated that HMBO often performs better than the original MBO and other population-based optimization algorithms such as ACO, BBO, DE, ES, GAPBIL, PSO and SGA. Moreover, the t-test result proved that the performance differences between the enhanced HMBO and the original MBO as well as the other optimization methods are statistically significant.

This article introduces a new variation of a known metaheuristic method for solving global optimization problems. The proposed algorithm is based on the Bat algorithm (BA), which is inspired by the micro-bat echolocation phenomenon, and addresses the problems of local-optima trapping using a special mutation operator that enhances the diversity of the standard BA, hence the name enhanced Bat algorithm (EBat). The design of EBat is introduced and its performance is evaluated against 24 of the standard benchmark functions, and compared to that of the standard BA, as well as to several well-established metaheuristic techniques. We also analyze the impact of different parameters on the EBat algorithm and determine the best combination of parameter values in the context of numerical optimization. The obtained results show that the new EBat method is indeed a promising addition to the arsenal of metaheuristic algorithms and can outperform several existing ones, including the original BA algorithm.

The aim of the study was to propose a new metaheuristic algorithm that combines parts of the well-known artificial bee colony (ABC) optimization with elements from the recent monarch butterfly optimization (MBO) algorithm. The idea is to improve the balance between the characteristics of exploration and exploitation in those algorithms in order to address the issues of trapping in local optimal solution, slow convergence, and low accuracy in numerical optimization problems. This article introduces a new hybrid approach by modifying the butterfly adjusting operator in MBO algorithm and uses that as a mutation operator to replace employee phase of the ABC algorithm. The new algorithm is called Hybrid ABC/MBO (HAM). The HAM algorithm is basically employed to boost the exploration versus exploitation balance of the original algorithms, by increasing the diversity of the ABC search process using a modified operator from MBO algorithm. The resultant design contains three components: The first and third component implements global search, while the second one performs local search. The proposed algorithm was evaluated using 13 benchmark functions and compared with the performance of nine metaheuristic methods from swarm intelligence and evolutionary computing: ABC, MBO, ACO, PSO, GA, DE, ES, PBIL, and STUDGA. The experimental results show that the HAM algorithm is clearly superior to the standard ABC and MBO algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value and convergence speed. The proposed HAM algorithm is a promising metaheuristic technique to be added to the repertory of optimization techniques at the disposal of researchers. The next step is to look into application fields for HAM.

A novel multi-population coevolution strategy for single objective immune optimization algorithm (MCIA) is proposed to solve numerical and engineering optimization problem in real world from the inspiration that how neuro-endocrine system affects T cells and B cells in immune system eliminate the danger. The main idea of MCIA is to promote three populations to coevolution through self-adjusted clone operator, the applied dislocation arithmetic crossover, cloud self-adapting mutation operator and local search operator to produce lymphocyte with high affinity, where several operators have the capability of broadening the elites search space, boosting the global and local search around elites in search space. The MCIA is population B, population T, and assistant population A carrying on parallel evolution in nature, which simulates the immune system more comprehensively and unique in the aspects: clone operator and selected elite elements in the memory population enable the search space be broadened and compressed, and with the help of the cloud model characterized with randomness and stable topotaxis and local search technique, the global and local search is integrated to find the global optima with high population diversity. The performance comparisons of MCIA with three known immune algorithms and three optimization algorithms in optimizing 12 benchmark functions indicate that MCIA is an effective algorithm for solving global optimization problems with high precision, good robustness and low time complexity.

Optimization is a classical issue and in many areas that are bound up with people’s daily life. In current decades, with the development of human civilization and industry society, many complicated optimization problems are raised. In the meantime, corresponding novel approaches are constantly proposed for solving these problems. One of them is meta-heuristics, which is inspired from natural phenomena and contains many kinds of algorithms. The classical meta-heuristic algorithms have exhibited their superiority in dealing optimization problems, especially for specific problems such as combinatorial optimization. As a novel meta-heuristic algorithm, biogeography-based optimization (BBO), inspired from the science of biogeography, has its own characteristics and exhibits a huge potential in computation and optimization. According to current investigations and analysis on this algorithm, it has not only achieved a great success in numerical optimization problems, but also been implemented in various kinds of applications, and drawn worldwide attentions. In this paper, we present a survey for this algorithm. First, we introduce the basic operators of BBO, including migration and mutation. For migration operator, it mimics species migration among islands, which provides a recombination way for candidate solutions to interact with each other so that the whole population can be improved. Besides linear migration model, several other popular migration models are also introduced and the corresponding performances are analyzed. For mutation operator, the design of BBO is different from other meta-heuristics. In standard BBO, different candidate solutions have different migration rates and the rate assignment is influential to BBO’s performance. Second, we summarized some popular variants of BBO and related hybrid algorithms that significantly enhance BBO’s performance. This part introduces the development of this algorithm and helps readers understand the way to choose a suitable version of BBO for a given problem. The way to improve algorithms’ performances helps readers design new variants of BBO for specific problems. Third, we present the evaluation of BBO’s performance for both numerical and practical problems. The results demonstrate BBO is competent to solve optimization problems. Despite so many achievements of BBO, some open issues that should be considered and solved in future work in order to make this algorithm more competitive in meta-heuristics.

The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function.