Search

Various optimization problems with multiple decision variables and complex constraints, which exist widely in the real world, are difficult to be solved by traditional methods. Brain storm optimization (BSO) algorithm, an advanced swarm intelligence optimization method, has high efficiency and flexibility in solving large-scale problems independent of problem characteristics. The essence of swarm intelligence optimization algorithm is that a population iteratively searches for the optimal solution in the solution space, and the process has randomness and blindness. To enhance the searching ability of BSO and strengthen the theoretical guidance of the algorithm, a brain storm optimization algorithm with feature information knowledge and learning mechanism (FIBSO) is proposed in this paper. In the process of BSO iteration, information interaction exists between individuals of each generation. The new individual is generated from the old individual, and the dominant individual contributes to the new individual. Theoretically, using the knowledge of characteristic information between individuals guides the evolution of the population in the dominant direction. Moreover, three search strategies guided by global and local optimal individuals are presented to balance the global and local search capabilities of the algorithm. The results of FIBSO and several comparison algorithms on the CEC2017 test suite indicate that FIBSO has superior performance to the state-of-the arts algorithms. The FIBSO is introduced to the no-wait flow shop scheduling problem, and the results show that FIBSO has the significant ability to solve practical engineering problems. [ABSTRACT FROM AUTHOR]

The distributed flow shop scheduling problem has become one of the key problems related to the high efficiency impacted factor in the manufacturing industry due to its typical scenarios in real-world industrial applications. In this paper, a knowledge-driven cooperative scatter search (KCSS) is proposed to address the distributed blocking flow shop scheduling problem (DBFSP) to minimize the makespan. The scatter search (SS) is adopted as the basic optimization framework in KCSS. The neighborhood perturbation operator and the Q-learning algorithm are combined to select the appropriate perturbation operator in the search process. Firstly, considering the complexity of distributed scenarios, five search operators are used to construct a disturbance strategy pool. Secondly, the Q-learning algorithm dynamically chooses disturbance strategies to enhance exploration ability and search efficiency. Afterward, a local search method based on neighborhood reconstruction is proposed to perturb the currently found optimal solution to strengthen the ability of KCSS to develop in local areas. In addition, the path relinking mechanism is introduced into the subset combination method to guarantee the diversity of solutions in the optimization process. Finally, the performance of the KCSS algorithm is verified on the benchmark set, and the experimental results demonstrate the robustness and effectiveness of the KCSS algorithm. In addition, 518 of the best-known solutions out of 720 benchmark instances are updated. [ABSTRACT FROM AUTHOR]

The iterated local search (ILS) is exceptionally successful in combinatorial solution spaces. However, few research works have reported on the application of ILS in continuous problems. In this article, a new hybrid population-based iterated local search (HILS) algorithm is proposed for solving numerical optimization problems. The proposed hybrid method introduces both success-history based parameter adaptation for differential evolution (SHADE) and limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) as the perturbation and local search strategy, respectively, and integrates the benefits of exploration capability of SHADE and local search performance of LBFGS. The simulated annealing type of acceptance criterion is adopted to balance the exploration and exploitation of ILS. The proposed HILS is tested against the CEC2017 benchmark functions, which were used to evaluate the performance of the proposed algorithm in solving numerical optimization problems. The experimental results show the effectiveness and efficiency of the HILS. [ABSTRACT FROM AUTHOR]

A novel hybrid optimization algorithm combining search area segmentation technique and the fast Fourier transform (HSAS/FFT) is presented to solve the numerical optimization problems. Firstly, the spectrum of each dimension of the objective function can be acquired by the FFT. The search space is segmented by using the spectrum to ensure that each subspace is unimodal. Secondly, the population of subspaces is produced and the optimal individual can be obtained by gradient descent algorithm. Finally, the local optimal solution in the optimal subspace is generated by the binary search algorithm. Make the optimal individual the new search space and repeat the process until meeting the termination condition. The proposed HSAS/FFT was tested on the CEC2017 benchmark, which evaluates the performance of the proposed algorithm on solving global optimization problems. Results obtained show that HSAS/FFT has an excellent performance and better convergence speed in comparison with some of the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The Gravitational Search Algorithm (GSA) has excellent performance in solving various optimization problems. However, it has been demonstrated that GSA tends to trap into local optima and are easy to lose diversity in the late evolution process. In this paper, a new hybrid algorithm based on self-adaptive Gravitational Search Algorithm (GSA) and Differential Evolution (DE) is proposed for solving single objective optimization, named SGSADE. Firstly, a self-adaptive mechanism based on GSA is proposed for improving the convergence speed and balancing exploration and exploitation. Secondly, the diversity of the population is maintained in the evolution process by using crossover and mutation operation from DE. Besides, to improve the performance of the algorithm, a new perturbation based on Levy flight theory is embedded to enhance exploitation capacity. The simulated results of SGSADE on 2017 CEC benchmark functions show that the SGSADE outperforms the state-of-the-art variant algorithms of the GSA. [ABSTRACT FROM AUTHOR]

The permutation flow shop scheduling problem (PFSSP), one of the most widely studied production scheduling problems, is a typical NP-hard combinatorial optimization problem. In this paper, a hybrid harmony search algorithm with efficient job sequence mapping scheme and variable neighborhood search (VNS), named HHS, is proposed to solve the PFFSP with the objective to minimize the makespan. First of all, to extend the HHS algorithm to solve the PFSSP effectively, an efficient smallest order value (SOV) rule based on random key is introduced to convert continuous harmony vector into a discrete job permutation after fully investigating the effect of different job sequence mapping schemes. Secondly, an effective initialization scheme, which is based on NEH heuristic mechanism combining with chaotic sequence, is employed with the aim of improving the solution’s quality of the initial harmony memory (HM). Thirdly, an opposition-based learning technique in the selection process and the best harmony (best individual) in the pitch adjustment process are made full use of to accelerate convergence performances and improve solution accuracy. Meanwhile, the parameter sensitivity is studied to investigate the properties of HHS, and the recommended values of parameters adopted in HHS are presented. Finally, by making use of a novel variable neighborhood search, the efficient insert and swap structures are incorporated into the HHS to adequately emphasize local exploitation ability. Experimental simulations and comparisons on both continuous and combinatorial benchmark problems demonstrate that the HHS algorithm outperforms the standard HS algorithm and other recently proposed efficient algorithms in terms of solution quality and stability. [ABSTRACT FROM AUTHOR]

• A novel hierarchical learning scatter search algorithm (TCSSMH) is proposed. • Multi-swarm hierarchical learning mechanism is introduced into SS to update Refset. • A cooperative interactive learning mechanism is introduced to update individuals. • A superior performance of the proposed approaches is proved in the experiments. Scatter search (SS) is a population-based metaheuristic algorithm, which has been proved high efficiency and effective optimizer for complex continuous real value problems. A two-stage cooperative SS guided with the multi-population hierarchical learning mechanism (TCSSMH) to overcome the slow convergence speed of the original SS is proposed. Three strategies are applied to the original SS. Firstly, TCSSMH adopts an adaptive two-way selection search strategy based on the elite reference set (RefSet), which is elite-oriented and ensures the quality of the population. Secondly, the multi-group hierarchical learning mechanism is embedded in the updating process of the RefSet , and the population of the candidates is divided into three levels including excellent candidates, medium candidates, and inferior candidates according to the fitness value of the function. These three subpopulations cooperate to balance the exploration and exploitation ability of the algorithm in the process of evolution. Finally, each subpopulation adopts an interactive learning strategy to increase the diversity of the population and avoid premature convergence of solutions. The optimum of each subpopulation with high accuracy is obtained by the pattern search (PS) optimization. The stronger search ability and higher search efficiency of these additional proposed strategies are verified by extensive experiments. The TCSSMH algorithm is tested on the CEC2017 benchmark test suite and practical engineering problems. The experimental results show that the TCSSMH algorithm is superior to other state-of-the-art algorithms in global search ability and convergence on the benchmark problems. [ABSTRACT FROM AUTHOR]

Harmony Search (HS) algorithm is a new population-based meta-heuristic which imitates the music improvisation process and has been successfully applied to a variety of combination optimization problems. In this paper, a self-adaptive harmony particle swarm optimization search algorithm, named SHPSOS, is proposed to solve global continuous optimization problems. Firstly, an efficient initialization scheme based on the PSO algorithm is presented for improving the solution quality of the initial harmony memory (HM). Secondly, a new self-adaptive adjusting scheme for pitch adjusting rate (PAR) and distance bandwidth (BW), which can balance fast convergence and large diversity during the improvisation step, are designed. PAR is dynamically adapted by symmetrical sigmoid curve, and BW is dynamically adjusted by the median of the harmony vector at each generation. Meanwhile, a new effective improvisation scheme based on differential evolution and the best harmony (best individual) is developed to accelerate convergence performance and to improve solution accuracy. Besides, Gaussian mutation strategy is presented and embedded in the SHPSOS algorithm to reinforce the robustness and avoid premature convergence in the evolution process of candidates. Finally, the global convergence performance of the SHPSOS is analyzed with the Markov model to testify the stability of algorithm. Experimental results on thirty-two standard benchmark functions demonstrate that SHPSOS outperforms original HS and the other related algorithms in terms of the solution quality and the stability. [ABSTRACT FROM AUTHOR]

Brain storm optimization algorithm (BSO), which is inspired by brain storm process of human, has been adopted as an efficient optimizer for various complex problems. A reinforcement learning brain storm optimization algorithm (RLBSO) to improve the performance of BSO is proposed in this paper. Four mutation strategies are designed to enhance the search capability of the algorithm in different stages. Elites are adopted as the guidance to ensure the quality of the population. The global best is utilized to guide the search direction of individuals. The cluster centers are employed to improve the exploitation ability of the algorithm. The historical individuals are utilized to the increase the diversity of the population. The Q-learning mechanism is introduced to guide the selection of strategies according to the historical information fed back by the corresponding strategies. A self-learning mechanism, which is based on the evolutionary state of the population and the experience of previous successful individuals, is utilized to determine the update method of individual. The RLBSO algorithm is tested on the CEC 2017 benchmark test suite and a practical engineering problem. The results show that RLBSO has better performance than the other state of the arts algorithms. • A reinforcement learning brain storm optimization algorithm (RLBSO) is proposed. • Four mutation strategies are introduced to increase the ability of global search. • Q-learning is utilized to guide the algorithm to the appropriate evolution strategy. • A self-learning strategy is utilized to determine the update method of the individual. • The RLBSO algorithm is tested on the CEC 2017 benchmark test suite. [ABSTRACT FROM AUTHOR]

The distributed assembly flow shop scheduling problem (DAFSP) is an important scenario in manufacturing system. In this paper, an optimal block knowledge driven backtracking search algorithm (BKBSA) is proposed to solve the distributed assembly No-wait flow shop scheduling problem (DANWFSP) with the objective of minimizing the completion time of assembly process. In BKBSA, three constructive heuristics are proposed to generate a competitive initial solution. Block-shifting based on block knowledge is embedded in the mutation strategy of BKBSA. The proposed block-shifting ensures that the optimal subsequence of a candidate solution is not destroyed in the mutation operation. The similarity between candidate solutions is utilized as feedback indicator to control the utilization of block-shifting. In addition, the VND algorithm based on factory-to-factory is proposed to further improve the optimal solution. Finally, the BKBSA and the other three state-of-the-art algorithms for DANWFSP are tested on 810 large-scale instances and 900 small-scale instances. The statistical analysis results show that BKBSA is an effective algorithm to solve DANWFSP. • An optimal block knowledge driven backtracking search algorithm (BKBSA) is proposed. • Three constructive heuristics are proposed to generate competitive initial solutions. • The Block-shifting based on optimal block knowledge is proposed. • The VND based on factory-to-factory is used to improve the optimal solution. • The combinations of parameters are guided by the DOE method. [ABSTRACT FROM AUTHOR]

To improve the performance of the backtracking search optimization algorithm (BSA), a multi-population cooperative evolution strategy guided BSA with hierarchical knowledge (HKBSA) is proposed in this paper. According to the domain knowledge of the candidates in objective space, the population is divided into the dominant population, the ordinary population and the inferior population. The information between the sub-populations has interacted with the evolution processes of the three sub-populations. The individuals in the dominant population are maintained as the optimal solutions and are utilized to guide the evolution of the other two sub-populations. A multi-strategy mutation mechanism is applied to solve non-separable problems. The distribution vector of inferior individuals is constructed by sampling, and a mechanism of the individual generation with feedback is proposed by combining self-learning strategy and elite learning strategy. The convergence of HKBSA is analyzed with the Markov model. Compared with the state-of-the-art BSA variants, HKBSA outperforms other algorithms in terms of the speed of convergence, solution accuracy and stability. • A Hierarchical Knowledge Guided Backtracking Search Algorithm (HKBSA) is proposed. • The population of HKBSA is divided into the dominant, ordinary and inferior population. • A multi-strategy mutation mechanism is applied to solve non-separable problems. • The self-learning and elite learning strategy are introduced to individual generation. • The convergence of HKBSA is analyzed with the Markov model. [ABSTRACT FROM AUTHOR]