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The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of multi-dimensional and multi-extreme value functions, the authors would introduce the extended memory factor to the PSO algorithm. Furthermore, the paper aims to improve the convergence rate and precision of basic artificial fish swarm algorithm (FSA), a novel FSA optimized by PSO algorithm with extended memory (PSOEM-FSA) is proposed.

In PSOEM-FSA, the extended memory for PSO is introduced to store each particle’ historical information comprising of recent places, personal best positions and global best positions, and a parameter called extended memory effective factor is employed to describe the importance of extended memory. Then, stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory. Furthermore, the extended memory factor is applied to five kinds of behavior pattern for FSA, including swarming, following, remembering, communicating and searching.

The paper proposes a new intelligent algorithm. On the one hand, this algorithm makes the fish swimming have the characteristics of the speed of inertia; on the other hand, it expands behavior patterns for the fish to choose in the search process and achieves higher accuracy and convergence rate than PSO-FSA, owning to extended memory beneficial to direction and purpose during search. Simulation results verify that these improvements can reduce the blindness of fish search process, improve optimization performance of the algorithm.

Because of the chosen research approach, the research results may lack persuasion. In the future study, the authors will conduct more experiments to understand the behavior of PSOEM-FSA. In addition, there are mainly two aspects that the performance of this algorithm could be further improved.

The proposed algorithm can be used to many practical engineering problems such as tracking problems.

The authors hope that the PSOEM-FSA can increase a branch of FSA algorithm, and enrich the content of the intelligent algorithms to some extent.

The novel optimized FSA algorithm proposed in this paper improves the convergence speed and searching precision of the ordinary FSA to some degree.

To improve the efficiency of end-of-life product’s disassembly process, this paper aims to propose a disassembly sequence planning (DSP) method to reduce additional efforts of removing parts when considering the changes of disassembly directions and tools.

The methodology has three parts. First, a disassembly hybrid graph model (DHGM) was adopted to represent disassembly operations and their precedence relations. After representing the problem as DHGM, a new integer programming model was suggested for the objective of minimizing the total disassembly time. The objective takes into account several criteria such as disassembly tools change and the change of disassembly directions. Finally, a novel hybrid approach with a chaotic mapping-based hybrid algorithm of artificial fish swarm algorithm (AFSA) and genetic algorithm (GA) was developed to find an optimal or near-optimal disassembly sequence.

Numerical experiment with case study on end-of-life product disassembly planning has been carried out to demonstrate the effectiveness of the designed criteria and the results exhibited that the developed algorithm performs better than other relevant algorithms.

More complex case studies for DSP problems will be introduced. The performance of the CAAFG algorithm can be enhanced by improving the design of AFSA and GA by combining them with other search techniques.

DSP of an internal gear hydraulic pump is analyzed to investigate the accuracy and efficiency of the proposed method.

This paper proposes a novel CAAFG algorithm for solving DSP problems. The implemented tool generates a feasible optimal solution and the considered criteria can help the planer obtain satisfactory results.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

The purpose of this paper is to improve the hydraulic efficiency without changing the overall dimension. The blade profile optimization design of the aero-centrifugal pump based on the biharmonic equation surrogate model has been studied.

First of all, Bezier curves and linear function are used to control the annular angle distribution and the stacking angle of blade profile under the MATLAB platform. Grid independence analysis has been studied to find the finest mesh scheme. After the precision comparison of test data and computation fluid dynamics 15 sets of design parameters are carried out as the boundary condition of the biharmonic equation. The efficiency surrogate model of the biharmonic equation is constructed via iteratively solving of a discrete difference equation. The other two surrogate models of response surface model (RSM) and radial basis function neural network surrogate model (RBFNNSM) are compared with the biharmonic equation surrogate model by the standard of modified complex correlation coefficient R² and root mean square deviation (RSME). Finally, the artificial fish swarm algorithm has been used to find the global optimal design parameters with the objective function of highest efficiency.

The results show that the design parameters code conversion method can reduce the number of optimization parameters from five to three, makes the design space become a cube, and compared with RSM and RBFNNSM, the biharmonic equation surrogate model has higher precision with R² is 0.8958, RSME is 0.1382. The final optimum result of AFSA is at the point of [1 −1 −1]. The internal flow field analysis shows that after optimization the outlet relative velocity becomes more uniform and the wake effect has been significantly decreased. The hydraulic efficiency of the optimized pump is about 59.45 per cent increasing 5.4 per cent compared with a prototype pump.

This study developed a new method to optimize the design parameters of aero-centrifugal pump impeller based on biharmonic equation surrogate model, which had a good agreement with experimental values within just 15 sets of the original design. The optimization results shows that the method can improve the hydraulic efficiency significantly.

To overcome the problems of premature convergence and lower local search ability of the Genetic Algorithm (GA), an improved GA based on the Fish Algorithm was proposed. The new algorithm can speed up the optimal and the cluster behavior of the GA with the ability to overcome the local maximum. Then the improved algorithm was applied to the optimization of the reinforced concrete frame structure. The optimization model was established with the objective of minimizing the cost of beams and columns. The total cost of the structure obtained from the algorithm was compared with that obtained using the fuzzy algorithm and found to be lower. Thus the improved GA is proven to be practical and efficient when used for the optimization of frame structures.

Biomimicry is an interdisciplinary approach inspired by the living beings in nature while searching for solutions to solve mankind’s problems. This new approach emerging in the late 1990s has been quite innovative while dealing with basic problem solving processes in a business environment. Biomimicry is a creative solution for such processes as design, transformation, organization and sustainability in business enterprises. The objective of this work is to offer model samples that build a bridge between the nature and business organizations. The principles in nature offer many strategies for a sustainable business performance and thus help us maintain optimization and effectiveness in business management through cooperation.

The purpose of this paper is to propose an enhanced elephant herding optimization (EEHO) algorithm by improving the exploration phase to overcome the fast-unjustified convergence toward the origin of the native EHO. The exploration and exploitation of the proposed EEHO are achieved by updating both clan and separation operators.

The original EHO shows fast unjustified convergence toward the origin specifically, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms. Furthermore, the star discrepancy measure is adopted to quantify the quality of the exploration phase of evolutionary algorithms in general.

In experiments, EEHO has shown a better performance of convergence rate compared with the original EHO. Reasons behind this performance are: EEHO proposes a more exploitative search method than the one used in EHO and the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Operator γ is added to EEHO assists to escape from local optima, which commonly exist in the search space. The proposed EEHO controls the convergence rate and the random walk independently. Eventually, the quantitative and qualitative results revealed that the proposed EEHO outperforms the original EHO.

Therefore, the pros and cons are reported as follows: pros of EEHO compared to EHO – 1) unbiased exploration of the whole search space thanks to the proposed update operator that fixed the unjustified convergence of the EHO toward the origin and the proposed separating operator that fixed the tendency of EHO to introduce new elephants at the boundary of the search space; and 2) the ability to control exploration–exploitation trade-off by independently controverting the convergence rate and the random walk using different parameters – cons EEHO compared to EHO: 1) suitable values for three parameters (rather than two only) have to be found to use EEHO.

As the original EHO shows fast unjustified convergence toward the origin specifically, the search method adopted in EEHO is more exploitative than the one used in EHO because of the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Further, the star discrepancy measure is adopted to quantify the quality of exploration phase of evolutionary algorithms in general. Operator γ that added EEHO allows the successive local and global searching (exploration and exploitation) and helps escaping from local minima that commonly exist in the search space.

The puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control.

The EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty.

This systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort.

The conventional real time issues such as nonlinearity characteristics, poor controllability and stability.

It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

This paper proposed a bi-level mathematical model for location, routing and allocation of medical centers to distribution depots during the COVID-19 pandemic outbreak. The developed model has two players including interdictor (COVID-19) and fortifier (government). Accordingly, the aim of the first player (COVID-19) is to maximize system costs and causing further damage to the system. The goal of the second player (government) is to minimize the costs of location, routing and allocation due to budget limitations.

The approach of evolutionary games with environmental feedbacks was used to develop the proposed model. Moreover, the game continues until the desired demand is satisfied. The Lagrangian relaxation method was applied to solve the proposed model.

Empirical results illustrate that with increasing demand, the values of the objective functions of the interdictor and fortifier models have increased. Also, with the raising fixed cost of the established depot, the values of the objective functions of the interdictor and fortifier models have raised. In this regard, the number of established depots in the second scenario (COVID-19 wave) is more than the first scenario (normal COVID-19 conditions).

The results of the current research can be useful for hospitals, governments, Disaster Relief Organization, Red Crescent, the Ministry of Health, etc. One of the limitations of the research is the lack of access to accurate information about transportation costs. Moreover, in this study, only the information of drivers and experts about transportation costs has been considered. In order to implement the presented solution approach for the real case study, high RAM and CPU hardware facilities and software facilities are required, which are the limitations of the proposed paper.

The main contributions of the current research are considering evolutionary games with environmental feedbacks during the COVID-19 pandemic outbreak and location, routing and allocation of the medical centers to the distribution depots during the COVID-19 outbreak. A real case study is illustrated, where the Lagrangian relaxation method is employed to solve the problem.

Collective intelligence has drawn many scientists’ attention in many centuries. This paper shows the collective intelligence study process in a perspective of crowd science.

After summarizing the time-order process of related researches, different points of views on collective intelligence’s measurement and their modeling methods were outlined.

The authors show the recent research focusing on collective intelligence optimization. The studies on application of collective intelligence and its future potential are also discussed.

This paper will help researchers in crowd science have a better picture of this highly related frontier interdiscipline.