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Grey prediction evolutionary algorithm (GPE) is an emerging category of meta-heuristic algorithm based on mathematical model. It generates offspring by predicting the macroscopic evolutionary trend of population series. On the contrary, the mining of local evolutionary trend are flawed. To address this imperfection, this paper introduces a quadratic function as a surrogate model to mine the local evolutionary trend of population series for the first time. Thereby an improved grey prediction evolutionary algorithm called grey prediction evolutionary algorithm with a surrogate model based on quadratic interpolation (GPE-QI) is designed. The surrogate model based on quadratic interpolation is regarded as an external auxiliary tool to mine the local evolutionary trend of population series by using currently superior individuals. Through utilizing the mined local trend information, a donor population closer to the optimal solution is formed to improve the prediction direction of the algorithm. The performance of GPE-QI is tested on CEC2019, CEC2020 benchmark functions and six engineering design problems. The experimental results demonstrate the stronger competitiveness of GPE-QI to well-established algorithms in terms of solving accuracy and convergence rate. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Improvement can be made by enhancing local search of grey prediction evolution. • Mine local evolutionary trend of population series by quadratic interpolation. • Construct a surrogate model by using the local evolutionary trend. • Design an improved grey prediction evolutionary algorithm with a surrogate model. • The proposed algorithm is more competitive than the well-established. [ABSTRACT FROM AUTHOR]

• Design of a dynamic mathematical model for blood assignment problem. • Proposal of a symbiotic optimization search algorithm for the BAP. • Implementation of two hybrid SOSGA and SOSSA algorithms to solve the BAP. • Suggestions for further development and possible areas of improvement. The shortage and wastage of blood products have been identified as the major contending factors that are frequently encountered in the management of blood supply chain processes. In general, the blood which is considered an essential product for which human existence relies on, has perishability characteristics that allow it to be stored up to a limited number of days. Therefore, this feature constrains the quantity of blood that can be retained in hospitals and blood centers, because keeping excessive number of blood units on inventory may result in blood product wastage. On the other hand, failure to stockpile on inventory can lead to shortage of this resource, and as a result may cause the cancellation of important activities such as treatment of special cases like surgery, accident, disaster circumstance and, in a worst case scenario increases the fatality rates at hospitals. This paper presents a dynamic mathematical model with the goal of improving the efficiency of blood related activities that occur at the blood centers. The model also caters for the assignment of whole blood units of available blood types to various requests. A set of equations that incorporate both the ABO and Rhesus blood groups are derived and presented subsequently. This further extends the initial work where only the ABO blood group was considered. In an effort to implement the developed model, three metaheuristic algorithms namely, symbiotic organisms search, symbiotic organisms search genetic algorithm, and symbiotic organisms search simulated annealing algorithms are proposed to identify the optimal routing for each of the blood types. An extensive numerical study was carried out using datasets from a synthetic blood sample collection process to illustrate the potential of the three metaheuristic algorithms to solve the developed blood assignment model. Furthermore, experimental results show that the hybrid symbiotic organisms search algorithms not only achieve superior accuracy, but also exhibits a higher level of stability, with the hybrid symbiotic organisms search genetic algorithm having the overall best superior performance. [ABSTRACT FROM AUTHOR]

• We address a complex single machine scheduling problem. • We consider flexible and variable maintenance activity to be scheduled. • We develop a novel self-adaptive harmony search algorithm. • We compare different heuristic and meta-heuristic algorithms. • We perform a sensitivity analysis involving several influencing factors. A common assumption in the scheduling literature provides that machines are always available, while often they have to be stopped because of preventive maintenance operations. Inspired by a real-life manufacturing context, this paper deals with the single machine scheduling problem with a flexible/variable maintenance to be executed within a specific time window. Differently from other studies in the literature, a set of non-resumable jobs with sequence dependent setup-times and non-zero release dates have to be scheduled along with a maintenance activity whose duration is a non-decreasing function of its starting time, with the objective of minimizing the total tardiness (T-problem). A mathematical model of the problem is proposed and, due to the high computational complexity, several heuristics have been implemented and tested with the aim of using some of them to make metaheuristic algorithms more effective and computationally efficient. Hence, a modified harmony search, powered by several reinforcement strategies and even equipped with a self-adaptive calibration mechanism, has been developed and compared with a set of additional algorithms proposed by the literature. Numerical results reveal that the modified harmony search outperforms the competing algorithms and no performance loss arises by adopting the self-adaptive mechanism. Finally, a proper sensitivity analysis allows assessing the impact of several influencing factors on the total tardiness minimization. [ABSTRACT FROM AUTHOR]

• A novel optimizer called Chimp Optimization Algorithm (ChOA) is proposed. • ChOA is inspired by individual intelligence and sexual motivation of chimps. • ChOA alleviates the problems of slow convergence rate and trapping in local optima. • The four main steps of Chimp hunting are implemented. This paper proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators. ChOA is designed to further alleviate the two problems of slow convergence speed and trapping in local optima in solving high-dimensional problems. In this paper, a mathematical model of diverse intelligence and sexual motivation of chimps is proposed. In this regard, four types of chimps entitled attacker, barrier, chaser, and driver are employed for simulating the diverse intelligence. Moreover, four main steps of hunting, i.e. driving, chasing, blocking, and attacking, are implemented. The proposed ChOA algorithm is evaluated in 3 main phases. First, a set of 30 mathematical benchmark functions is utilized to investigate various characteristics of ChOA. Secondly, ChOA was tested by 13 high-dimensional test problems. Finally, 10 real-world optimization problems were used to evaluate the performance of ChOA. The results are compared to several newly proposed meta-heuristic algorithms in terms of convergence speed, the probability of getting stuck in local minimums, and exploration, exploitation. Also, statistical tests were employed to investigate the significance of the results. The results indicate that the ChOA outperforms the other benchmark optimization algorithms. [ABSTRACT FROM AUTHOR]

• The energy-oriented activity splitting and recombining proactively. • Dynamic fuzzy clustering strategy for the time-varying resource consumption level. • Metaheuristic solution for project scheduling with different granularity levels. Energy-cost-aware scheduling in manufacturing process has recently been considered as an effective way to use energy. In addition, applying time-of-use is regarded as one of the optimal uses of energy which the governments extend to control energy consumption. Complex product system (CoPS) has more complex and huge amounts of multiple manufacturing activities with higher energy sensibility in project scheduling than commodity products. This paper presents a new variant of resource-constrained project scheduling problem (RCPSP), which is named energy-cost-aware resource-constrained project scheduling problem with activity splitting and recombining (eRCPSP-AS&R) for CoPS. In the proposed model, a dynamic activity splitting and recombining strategy for CoPS is used by considering energy consumption. Also, a bi-objective (project delay and energy consumption cost) mathematical model is established. The solution framework of eRCPSP-AS&R model consists of two parts: activity splitting and recombining, project scheduling. The dynamic strategy of activity splitting and recombining is used to perform energy-cost-aware based on hybrid intuitionistic fuzzy information entropy, TOPSIS and fuzzy clustering method. Moreover, an improved NSGA-II algorithm is employed to solve the Pareto-optimal solutions for the proposed bi-objective optimization problem. Series of computational experiments are conducted and verified the overall performance of proposed eRCPSP-AS&R model and approaches. [ABSTRACT FROM AUTHOR]

The problem of maximizing dispersion requires the selection of a specific number of elements from a given set, in such a way that the minimum distance between the pairs of selected elements is maximized. In recent years, this problem has received a lot of attention and has been solved with many complex heuristics. However, there is a recent variant in which the selected elements have to satisfy two realistic constraints, a minimum capacity limit and a maximum budget, which in spite of its practical significance in facility location, has received little attention. In this paper, we first propose mathematical models to obtain the optimal solution of small- and medium-size instances, and then present new metaheuristic procedures for finding approximate solutions to target large-size instances. Specifically, we develop a GRASP and a tabu search to obtain high quality solutions in short computational times. We perform extensive experimentation to compare our heuristic proposals with the optimal solutions obtained with the models applied to the Gurobi optimizer, as well as with a previous heuristic. Statistical tests confirm the superiority of our methods. [ABSTRACT FROM AUTHOR]