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This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]

Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Operational amplifiers (Op-Amps) are critical to sensor systems because they enable precise, reliable, and flexible signal processing. Current automated Op-Amp generation methods suffer from extremely low efficiency because the time-consuming SPICE-in-the-loop sizing is normally involved as its inner loop. In this paper, we propose an efficiently automated Op-Amp generation tool using a hybrid sizing method, which combines the merits together from a deterministic optimization algorithm and differential evolution algorithm. Thus, it can not only quickly find a decent local optimum, but also eventually converge to a global optimum. This feature is well fit to be serving as an acute filter in the circuit structure evaluation flow to efficiently eliminate any undesirable circuit structures in advance of detailed sizing. Our experimental results demonstrate its superiority over traditional sizing approaches and show its efficacy in highly boosting the efficiency of automated Op-Amp structure generation. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index ( Q A B / F ), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure ( N A B / F ). The average Q A B / F = 0.765508 , S C D = 1.63185 , S S I M = 0.726317 , and N A B / F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon's and Friedman's tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test. [ABSTRACT FROM AUTHOR]

With the rapid development of new power systems, diverse new power services have imposed stricter requirements on network resources and performance. However, the traditional method of transmitting request data to the IoT management platform for unified processing suffers from large delays due to long transmission distances, making it difficult to meet the delay requirements of new power services. Therefore, to reduce the transmission delay, data transmission, storage and computation need to be performed locally. However, due to the limited resources of individual nodes in the local power wireless communication network, issues such as tight coupling between devices and resources and a lack of flexible allocation need to be addressed. The collaborative allocation of resources among multiple nodes in the local network is necessary to satisfy the multi-dimensional resource requirements of new power services. In response to the problems of limited node resources, inflexible resource allocation, and the high complexity of multi-dimensional resource allocation in local power wireless communication networks, this paper proposes a multi-objective joint optimization model for the collaborative allocation of communication, storage, and computing resources. This model utilizes the computational characteristics of communication resources to reduce the dimensionality of the objective function. Furthermore, a mouse swarm optimization algorithm based on multi-strategy improvements is proposed. The simulation results demonstrate that this method can effectively reduce the total system delay and improve the utilization of network resources. [ABSTRACT FROM AUTHOR]

This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars' attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

Metaheuristics are commonly employed as a means of solving many distinct kinds of optimization problems. Several natural-process-inspired metaheuristic optimizers have been introduced in the recent years. The convergence, computational burden and statistical relevance of metaheuristics should be studied and compared for their potential use in future algorithm design and implementation. In this paper, eight different variants of dragonfly algorithm, i.e. classical dragonfly algorithm (DA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA), and hybridization of dragonfly algorithm and artificial bee colony (HDA) are applied to solve four industrial chemical process optimization problems. A fuzzy multi-criteria decision making tool in the form of fuzzy-measurement alternatives and ranking according to compromise solution (MARCOS) is adopted to ascertain the relative rankings of the DA variants with respect to computational time, Friedman's rank based on optimal solutions and convergence rate. Based on the comprehensive testing of the algorithms, it is revealed that DADE, QGDA and classical DA are the top three DA variants in solving the industrial chemical process optimization problems under consideration. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

With the rapid development of the geometric modeling industry and computer technology, the design and shape optimization of complex curve shapes have now become a very important research topic in CAGD. In this paper, the Hybrid Artificial Hummingbird Algorithm (HAHA) is used to optimize complex composite shape-adjustable generalized cubic Ball (CSGC–Ball, for short) curves. Firstly, the Artificial Hummingbird algorithm (AHA), as a newly proposed meta-heuristic algorithm, has the advantages of simple structure and easy implementation and can quickly find the global optimal solution. However, there are still limitations, such as low convergence accuracy and the tendency to fall into local optimization. Therefore, this paper proposes the HAHA based on the original AHA, combined with the elite opposition-based learning strategy, PSO, and Cauchy mutation, to increase the population diversity of the original algorithm, avoid falling into local optimization, and thus improve the accuracy and rate of convergence of the original AHA. Twenty-five benchmark test functions and the CEC 2022 test suite are used to evaluate the overall performance of HAHA, and the experimental results are statistically analyzed using Friedman and Wilkerson rank sum tests. The experimental results show that, compared with other advanced algorithms, HAHA has good competitiveness and practicality. Secondly, in order to better realize the modeling of complex curves in engineering, the CSGC–Ball curves with global and local shape parameters are constructed based on SGC–Ball basis functions. By changing the shape parameters, the whole or local shape of the curves can be adjusted more flexibly. Finally, in order to make the constructed curve have a more ideal shape, the CSGC–Ball curve-shape optimization model is established based on the minimum curve energy value, and the proposed HAHA is used to solve the established shape optimization model. Two representative numerical examples comprehensively verify the effectiveness and superiority of HAHA in solving CSGC–Ball curve-shape optimization problems. [ABSTRACT FROM AUTHOR]

This paper focuses on achieving the low-cost coexistence of the networks in an unlicensed spectrum by making them operate on non-overlapping channels. For achieving this goal, we first give a universal convergence analysis framework for the unlicensed spectrum allocation algorithm. Then, a one-timescale iteration-adjustable unlicensed spectrum allocation algorithm is developed, where the step size and timescale parameter can be jointly adjusted based on the system performance requirement and signal overhead concern. After that, we derive the sufficient condition for the one-timescale algorithm. Furthermore, the upper bound of convergence error of the one-timescale spectrum allocation algorithm is obtained. Due to the multi-timescale evolution of the network states in the wireless network, we further propose a two-timescale iteration-adjustable joint frequency selection and frequency allocation algorithm, where the frequency selection iteration timescale is set according to the slow-changing statistical channel state information (CSI), whereas the frequency allocation iteration timescale is set according to the fast-changing local CSI. Then, we derive the convergence condition of two-timescale algorithms and the upper bound of the corresponding convergence error. The experimentalresults show that the small timescale adjustment parameter and large step size can help decrease the convergence error. Moreover, compared with traditional algorithms, the two-timescale policy can achieve throughput similar to traditional algorithms with very low iteration overhead. [ABSTRACT FROM AUTHOR]

Multi-objective problems have gained much attention during the last decade. To balance the diversity and the convergence of the multi-objective differential evolution algorithm (MODE), an improved MODE is proposed based on the affinity propagation clustering (APC) and the non-dominated count approach in this paper. The proposed algorithm is referred to as AP-MODE, which improves the search efficiency by utilizing the affinity propagation approach to find out the population distribution structure for guiding search. In addition, mating restriction probability is used to select parent individuals for recombination from the neighborhoods or the whole population. Meanwhile, the mating restriction probability is updated according to the non-dominated count approach at each generation. This proposed algorithm is verified by comparing it with some state-of-the-art multi-objective evolutionary algorithms, and the simulation results on DTLZ test problems indicate that AP-MODE can efficiently achieve two goals of multi-objective optimization, i.e., the convergence to actual Pareto front and uniform spread of individuals along Pareto front. [ABSTRACT FROM AUTHOR]

Variational Quantum Algorithms (VQAs) are among the most promising NISQ-era algorithms for harnessing quantum computing in diverse fields. However, the underlying optimization processes within these algorithms usually deal with local minima and barren plateau problems, preventing them from scaling efficiently. Our goal in this paper is to study alternative optimization methods that can avoid or reduce the effect of these problems. To this end, we propose to apply the Differential Evolution (DE) algorithm to VQAs optimizations. Our hypothesis is that DE is resilient to vanishing gradients and local minima for two main reasons: (1) it does not depend on gradients, and (2) its mutation and recombination schemes allow DE to continue evolving even in these cases. To demonstrate the performance of our approach, first, we use a robust local minima problem to compare state-of-the-art local optimizers (SLSQP, COBYLA, L-BFGS-B and SPSA) against DE using the Variational Quantum Eigensolver algorithm. Our results show that DE always outperforms local optimizers. In particular, in exact simulations of a 1D Ising chain with 14 qubits, DE achieves the ground state with a 100% success rate, while local optimizers only exhibit around 40%. We also show that combining DE with local optimizers increases the accuracy of the energy estimation once avoiding local minima. Finally, we demonstrate how our results can be extended to more complex problems by studying DE performance in a 1D Hubbard model. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a proficient optimizer and has been broadly implemented in real life applications of various fields. Several mutation based adaptive approaches have been suggested to improve the algorithm efficiency in recent years. In this paper, a novel self-adaptive method called SaMDE has been designed and implemented on the mutation-based modified DE variants such as modified randomized localization-based DE (MRLDE), donor mutation based DE (DNDE), and sequential parabolic interpolation based DE (SPIDE), which were proposed by the authors in previous research. Using the proposed adaptive technique, an appropriate mutation strategy from DNDE and SPIDE can be selected automatically for the MRLDE algorithm. The experimental results on 50 benchmark problems taken of various test suits and a real-world application of minimization of the potential molecular energy problem validate the superiority of SaMDE over other DE variations. [ABSTRACT FROM AUTHOR]

Variational Quantum Algorithms (VQAs) are among the most promising NISQ-era algorithms for harnessing quantum computing in diverse fields. However, the underlying optimization processes within these algorithms usually deal with local minima and barren plateau problems, preventing them from scaling efficiently. Our goal in this paper is to study alternative optimization methods that can avoid or reduce the effect of these problems. To this end, we propose to apply the Differential Evolution (DE) algorithm to VQAs optimizations. Our hypothesis is that DE is resilient to vanishing gradients and local minima for two main reasons: (1) it does not depend on gradients, and (2) its mutation and recombination schemes allow DE to continue evolving even in these cases. To demonstrate the performance of our approach, first, we use a robust local minima problem to compare state-of-the-art local optimizers (SLSQP, COBYLA, L-BFGS-B and SPSA) against DE using the Variational Quantum Eigensolver algorithm. Our results show that DE always outperforms local optimizers. In particular, in exact simulations of a 1D Ising chain with 14 qubits, DE achieves the ground state with a 100% success rate, while local optimizers only exhibit around 40%. We also show that combining DE with local optimizers increases the accuracy of the energy estimation once avoiding local minima. Finally, we demonstrate how our results can be extended to more complex problems by studying DE performance in a 1D Hubbard model. [ABSTRACT FROM AUTHOR]

This paper presents the minimization of total harmonic distortion in a seven-level cascaded H-bridge multilevel inverter with resistive load using the teaching–learning-based optimization algorithm. The minimization of Total Harmonic Distortion (THD)is a challenging optimization problem due to the fact that nonlinear equations are involved. Recently, bio-inspired algorithms have become very popular approaches to solving various optimization problems in different areas of engineering. For this reason, the results obtained with the Teaching–Learning-Based Optimization (TLBO)algorithm were compared with three other popular bio-inspired algorithms, the genetic algorithm, differential evolution, and particle swarm optimization. The comparative analysis, conducted by sweeping the modulation index, made it possible to obtain graphs and data on the behavior of the four analyzed algorithms. Finally, it was concluded that the TLBO algorithm is very effective and is able to solve the THD-minimization problem. Its main advantage over the other algorithms is the fact that it does not require control parameters for its correct operation in the solution of the problem. [ABSTRACT FROM AUTHOR]

The differential evolution (DE) algorithm is a simple and efficient population-based evolutionary algorithm. In DE, the mutation strategy and the control parameter play important roles in performance enhancement. However, single strategy and fixed parameter are not universally applicable to problems and evolution stages with diverse characteristics; besides, the weakness of the advanced DE optimization framework, called selective-candidate framework with similarity selection rule (SCSS), is found by focusing on its single strategy and fixed parameter greedy degree (GD) setting. To address these problems, we mainly combine the multiple candidates generation with multi-strategy (MCG-MS) and the adaptive similarity selection (ASS) rule. On the one hand, in MCG-MS, two symmetrical mutation strategies, "DE/current-to-pbest-w/1" and designed "DE/current-to-cbest-w/1", are utilized to build the multi-strategy to produce two candidate individuals, which prevents the over-approximation of the candidate in SCSS. On the other hand, the ASS rule provides the individual selection mechanism for multi-strategy to determine the offspring from two candidates, where parameter GD is designed to increase linearly with evolution to maintain diversity at the early evolution stage and accelerate convergence at the later evolution stage. Based on the advanced algorithm jSO, replacing its offspring generation strategy with the combination of MCG-MS and ASS rule, this paper proposes multi-strategy differential evolution algorithm with adaptive similarity selection rule (MSDE-ASS). It combines the advantages of two symmetric strategies and has an efficient individual selection mechanism without parameter adjustment. MSDE-ASS is verified under the Congress on Evolutionary Computation (CEC) 2017 competition test suite on real-parameter single-objective numerical optimization, and the results indicate that, of the 174 cases in total, it wins in 81 cases and loses in 30 cases, and it has the smallest performance ranking value, of 3.05. Therefore, MSDE-ASS stands out compared to the other state-of-the-art DEs. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal design of different VLSI circuits, namely, the CMOS voltage reference circuit and the CMOS ring oscillator (RO). The optimization technique used here is the multiobjective differential evolution algorithm (MDEA). All the circuits are designed for 90 nm technology. The main objective of the CMOS voltage reference circuit is to minimize the voltage variation at the output. The targeted value of the reference voltage is 550 mV. A CMOS ring oscillator (RO) is designed depending on the performance parameters such as power consumption and phase noise. The optimal transistor sizing of each circuit is obtained from MDEA. Each circuit is implemented in SPICE by taking the optimal dimensions of the transistors, and the performance parameters are achieved. The designed voltage reference circuit achieves a reference voltage of 550 mV with 600 nW power dissipation. The reference voltage variation of 8.18% is observed due to temperature variation from −40°C to + 125°C. The MDEA-based optimal design of RO oscillates at 2.001 GHz frequency, has a phase noise of −87 dBc/Hz at 1 MHz offset frequency, and consumes 71 μW power. This work mainly aims to optimize the MOS transistors' sizes using MDEA for better circuit performance parameters. SPICE simulation has been carried out by using the optimal values of MOS transistor sizes to exhibit the performance parameters of the circuit. Simulation results establish that design specifications are closely met. SPICE results show that MDEA is a better technique for the optimal design of the above-mentioned VLSI circuits. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most popular and widely used optimizers among the community of evolutionary computation. Despite numerous works having been conducted on the improvement of DE performance, there are still some defects, such as premature convergence and stagnation. In order to alleviate them, this paper presents a novel DE variant by designing a new mutation operator (named "DE/current-to-pbest_id/1") and a new control parameter setting. In the new operator, the fitness value of the individual is adopted to determine the chosen scope of its guider among the population. Meanwhile, a group-based competitive control parameter setting is presented to ensure the various search potentials of the population and the adaptivity of the algorithm. In this setting, the whole population is randomly divided into multiple equivalent groups, the control parameters for each group are independently generated based on its location information, and the worst location information among all groups is competitively updated with the current successful parameters. Moreover, a piecewise population size reduction mechanism is further devised to enhance the exploration and exploitation of the algorithm at the early and later evolution stages, respectively. Differing from the previous DE versions, the proposed method adaptively adjusts the search capability of each individual, simultaneously utilizes multiple pieces of successful parameter information to generate the control parameters, and has different speeds to reduce the population size at different search stages. Then it could achieve the well trade-off of exploration and exploitation. Finally, the performance of the proposed algorithm is measured by comparing with five well-known DE variants and five typical non-DE algorithms on the IEEE CEC 2017 test suite. Numerical results show that the proposed method is a more promising optimizer. [ABSTRACT FROM AUTHOR]

The success-history based adaptive differential evolution (SHADE) algorithm is an efficient modified version of the differential evolution (DE) algorithm, and it has been successfully applied to solve some real-world optimization problems. However, to the best of our knowledge, it has been rarely applied in the field of structural optimization. The optimal design of structures with frequency constraints is well known as a highly nonlinear and non-convex optimization problem with many local optima. In this paper, the SHADE algorithm is examined in the context of size optimization of large-scale truss structures with multiple frequency constraints. In SHADE, a historical memory of successful control parameter settings is used to guide the generation of new control parameters. In order to demonstrate the effectiveness and efficiency of SHADE, three truss optimization problems with multiple frequency constraints are presented. The three examples considered in this paper include a 600-bar single-layer dome-shaped truss, a 1180-bar single-layer dome-shaped truss, and a 1410-bar double-layer dome-shaped truss. The results obtained by the SHADE algorithm are presented and compared with the best-known results reported in the literature. Numerical results indicate the effectiveness and superior performance of SHADE over other algorithms in terms of solution accuracy and robustness. It is worth mentioning that in all the three cases considered, the optimal designs obtained by SHADE are the best ones reported in the literature so far. However, SHADE often requires fewer structural analyses than those required by the other algorithms. [ABSTRACT FROM AUTHOR]

Complex systems provide an opportunity to analyze the essence of phenomena by studying their intricate connections. The networks formed by these connections, known as complex networks, embody the underlying principles governing the system's behavior. While complex networks have been previously applied in the field of evolutionary computation, prior studies have been limited in their ability to reach conclusive conclusions. Based on our investigations, we are against the notion that there is a direct link between the complex network structure of an algorithm and its performance, and we demonstrate this experimentally. In this paper, we address these limitations by analyzing the dynamic complex network structures of five algorithms across three different problems. By incorporating mathematical distributions utilized in prior research, we not only generate novel insights but also refine and challenge previous conclusions. Specifically, we introduce the biased Poisson distribution to describe the algorithm's exploration capability and the biased power-law distribution to represent its exploitation potential during the convergence process. Our aim is to redirect research on the interplay between complex networks and evolutionary computation towards dynamic network structures, elucidating the essence of exploitation and exploration in the black-box optimization process of evolutionary algorithms via dynamic complex networks. [ABSTRACT FROM AUTHOR]

This paper uses a fuzzy set scheme to present an adaptive fractional differential algorithm for image edge enhancement and texture preservation. In the proposed algorithm, an image's membership function and area feature are used to calculate the fuzzy set of images. The function of adaptive fractional differential order (FAFDO) can be constructed by making the linear transformation of the fuzzy set. Then, the fuzzy adaptive fractional differential mask (FAFDM) is obtained by substituting the FAFDO into the fractional differential mask. Finally, the image edge and texture are enhanced and preserved by applying airspace filtering of the FAFDM convolution. The experimental results show that, compared to fractional differential or fuzzy set‐based image enhancement algorithms, the proposed algorithm can adaptively enhance the image edge and preserve the image texture by analysing the fuzziness of the image itself. [ABSTRACT FROM AUTHOR]

In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm's capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA's performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a study on improving the estimation accuracy and convergence rate of hysteresis modeling of MFC actuators using mutation enhanced differential evolution (MEDE) algorithm, a modified version of the differential evolution algorithm. The proposed MEDE algorithm uses three mutation strategies, i.e., best, rand, and pbest. To model the secondary path of a smart flexible beam with MFC actuators, a Hammerstein model that combines an asymmetric Bouc-Wen model with an ARX model connected in series is proposed. The fitness function values of the Hammerstein model are compared with evolutionary algorithms. [ABSTRACT FROM AUTHOR]

To reduce logistics scheduling costs and energy consumption, this paper studies the slab allocation and hot-rolling scheduling integrated optimization problem that arises in practical iron and steel enterprises. In this problem, slabs are first allocated to orders and then sent to heating furnaces for heating; then, they are sent to a hot-rolling mill for rolling. A 0–1 integer programming model is established to minimize the attribute difference in the allocation cost between slabs and orders, the switching cost of hot-rolling processing, and waiting times after slabs reach rolling mills. Given the problem's characteristics, an improved differential evolution algorithm using a real-number coding method is designed to solve it. Three different heuristic algorithms are proposed to improve the quality of solutions in the initial population. Multiple parent individuals participate in the mutation operation, which increases the population diversity and prevents the algorithm from falling into the local optimum prematurely. Experiments on 14 sets of real production data from a large domestic iron and steel plant show that our improved differential evolution algorithm generates significantly better solutions in a reasonable amount of time compared with CPLEX, the simulated artificial method, and the classical differential evolution algorithm, and it can be used by practitioners. [ABSTRACT FROM AUTHOR]

As social and environmental issues become increasingly serious, both fuel costs and environmental impacts should be considered in the cogeneration process. In recent years, combined heat and power economic emission dispatch (CHPEED) has become a crucial optimization problem in power system management. In this paper, a novel reinforcement-learning-based multi-objective differential evolution (RLMODE) algorithm is suggested to deal with the CHPEED problem considering large-scale systems. In RLMODE, a Q-learning-based technique is adopted to automatically adjust the control parameters of the multi-objective algorithm. Specifically, the Pareto domination relationship between the offspring solution and the parent solution is used to determine the action reward, and the most-suitable algorithm parameter values for the environment model are adjusted through the Q-learning process. The proposed RLMODE was applied to solve four CHPEED problems: 5, 7, 100, and 140 generating units. The simulation results showed that, compared with four well-established multi-objective algorithms, the RLMODE algorithm achieved the smallest cost and smallest emission values for all four CHPEED problems. In addition, the RLMODE algorithm acquired better Pareto-optimal frontiers in terms of convergence and diversity. The superiority of RLMODE was particularly significant for two large-scale CHPEED problems. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a simple, effective, and robust algorithm, which has demonstrated excellent performance in dealing with global optimization problems. However, different search strategies are designed for different fitness landscape conditions to find the optimal solution, and there is not a single strategy that can be suitable for all fitness landscapes. As a result, developing a strategy to adaptively steer population evolution based on fitness landscape is critical. Motivated by this fact, in this paper, a novel adaptive DE based on fitness landscape (FL-ADE) is proposed, which utilizes the local fitness landscape characteristics in each generation population to (1) adjust the population size adaptively; (2) generate DE/current-to-pcbest mutation strategy. The adaptive mechanism is based on local fitness landscape characteristics of the population and enables to decrease or increase the population size during the search. Due to the adaptive adjustment of population size for different fitness landscapes and evolutionary processes, computational resources can be rationally assigned at different evolutionary stages to satisfy diverse requirements of different fitness landscapes. Besides, the DE/current-to-pcbest mutation strategy, which randomly chooses one of the top p% individuals from the archive cbest of local optimal individuals to be the pcbest, is also an adaptive strategy based on fitness landscape characteristic. Using the individuals that are approximated as local optimums increases the algorithm's ability to explore complex multimodal functions and avoids stagnation due to the use of individuals with good fitness values. Experiments are conducted on CEC2014 benchmark test suit to demonstrate the performance of the proposed FL-ADE algorithm, and the results show that the proposed FL-ADE algorithm performs better than the other seven highly performing state-of-art DE variants, even the winner of the CEC2014 and CEC2017. In addition, the effectiveness of the adaptive population mechanism and DE/current-to-pcbest mutation strategy based on landscape fitness proposed in this paper are respectively verified. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the city scale and the increasing population, people have put forward higher requirements for the speed, reliability and economy of logistics distribution. Although there have been many achievements in the research on the vehicle optimal scheduling decision model and its solution algorithm, there are some problems such as low solution efficiency, long time consumption, and easy to fall into the local optimal solution. The purpose of this paper is to study the model of vehicle scheduling problem in distribution center based on improved differential evolution algorithm. This paper firstly makes a simple analysis of the vehicle distribution route optimization problem, and points out the main factors that constitute the route optimization problem; then analyzes the principle of the differential evolution algorithm, and on this basis, the differential evolution algorithm is improved. In the experimental part, this paper takes a distribution center as the research object, and proposes the implementation steps of the algorithm of the vehicle scheduling problem based on the improved differential evolution algorithm. And test the running cost of the improved differential evolution algorithm and differential evolution algorithm. Experimental results show that applying the improved differential evolution algorithm to vehicle scheduling problems has certain practicality. When the number of iterations reaches 60, the running cost of the improved differential evolution algorithm is 1,200 yuan, and the running cost of the differential evolution algorithm is 1,300 yuan. [ABSTRACT FROM AUTHOR]

The soil water retention curve (SWRC) has a significant role in determining the unsaturated properties of soil. A stochastic optimisation algorithm named Rao-1 algorithm is employed to estimate the parameters of the SWRC model in this paper. The Rao-1 algorithm is a simple heuristic search algorithm containing only addition and multiplication operations. This paper introduces the method and its application in determining soil water retention this model parameters in detail. In this study, the van Genuchten model is used to depict the SWRC for its good fitting capacity, and the van Genuchten model parameters are determined using Rao-1 algorithm. The feasibility and efficiency of the proposed method are validated via the experimental results of 24 soil samples of 12 soil textural classes. Besides, the performance of Rao-1 algorithm is compared with that of salp swarm algorithm, the particle swarm optimization algorithm, differential evolution algorithm, and RETC program. Through comparative analysis, Rao-1 algorithm outperforms other methods in determining SWRC parameters. [ABSTRACT FROM AUTHOR]

According to the World Health Organization, cancer is a worldwide health problem. Its high mortality rate motivates scientists to study new treatments. One of these new treatments is hyperthermia using magnetic nanoparticles. This treatment consists in submitting the target region with a low-frequency magnetic field to increase its temperature over 43 °C, as the threshold for tissue damage and leading the cells to necrosis. This paper uses an in silico three-dimensional Pennes' model described by a set of partial differential equations (PDEs) to estimate the percentage of tissue damage due to hyperthermia. Differential evolution, an optimization method, suggests the best locations to inject the nanoparticles to maximize tumor cell death and minimize damage to healthy tissue. Three different scenarios were performed to evaluate the suggestions obtained by the optimization method. The results indicate the positive impact of the proposed technique: a reduction in the percentage of healthy tissue damage and the complete damage of the tumors were observed. In the best scenario, the optimization method was responsible for decreasing the healthy tissue damage by 59 % when the nanoparticles injection sites were located in the non-intuitive points indicated by the optimization method. The numerical solution of the PDEs is computationally expensive. This work also describes the implemented parallel strategy based on CUDA to reduce the computational costs involved in the PDEs resolution. Compared to the sequential version executed on the CPU, the proposed parallel implementation was able to speed the execution time up to 84.4 times. [ABSTRACT FROM AUTHOR]

In this paper, the hybrid differential evolution and symbiotic organism search (HDS), is the first-time developed for general solutions of a piezoelectric stack in ultrasonic transducers. The convergence and reliability of the new algorithm are verified through comparison with corresponding data from similar previous publications and differential evolution (DE) algorithm. This study also presents and discusses the calculation results using HDS for commercial piezoelectric stacks. The Matlab HDS programs for a segmented piezoelectric (PZT) model have advanced features including its applicability to any configurations, thickness and arbitrary layer numbers of PZT. Using the novel proposed technique, there is no requirement for initial data guess, no limitations for piezoelectric stacks and the convergence rate is much faster than DE. Therefore, the HDS is promising for direct evaluation of specific aging or degradation mechanisms of ultrasonic transducers. [ABSTRACT FROM AUTHOR]

The swarm planar underactuated robots have the ability to organize each robot to complete task in a finite time and the characteristics of ignoring gravity, energy saving, light weight, and so on. In this paper, we propose control strategy for such robot via motion planning and intelligent algorithm. First, we establish a unified dynamic model and analysis its underactuated characteristics. In order to enable all links to reach the target position smoothly in a finite time, a suitable trajectory is planned and parameters are optimized by the differential evolution algorithm (DEA). Then, the controllers are designed for each planar underactuated robot. Finally, the simulation results show that the proposed strategy is effective. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Multi-furnace scheduling simultaneously is an important part to increase productivity and reduce the production cost in single crystal silicon enterprises. In the restrained power consumption requirements environment, the optimal sequencing of process operation start-time for single crystal furnaces is a challenging problem. To solve this problem, the scheduling model of multi-furnace scheduling is established in this paper to minimize the maximum completion time. Then, an improved DE algorithm called the multi-strategy individual adaptive mutation differential evolution algorithm (MSIADE) is presented to address the scheduling model. In the improved DE algorithm, the different dimensional and multi-strategy mutation operations are adopted to refrain the algorithm from the local optimal, then the different mutation factors are assigned to each individual through the rank of fitness function value to strengthen the exploration ability of the MSIADE algorithm. Simulation experiments results based on the standard test functions and the established scheduling model show the feasibility in the established model and the effectiveness in the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a modified and hybrid flower pollination algorithms (MHFPA) is proposed for dealing with the multi-objective optimal power flow (MOOPF) problem with conflictive objectives. The algorithm combines the mutation and crossover process in the differential evolution (DE) algorithm, introduces the sinusoidal nonlinear dynamic switching probability (SNDSP) and the elite strategy of elder generation (ESEG), which can improve the shortcomings of the original pollen algorithm that it is easy to fall into the local optimum and the diversity is insufficient. A screening approach with Pareto-dominant rule (SAPR) is proposed to ensure that the state variable can satisfy the inequality constraints of the power system. A uniformly distributed Pareto optimal set (POS) is obtained by the non-dominant sorting with elite strategy (NSES) based on Rank and Density estimation, and the best trade-off solution (BTS) is determined from the POS obtained by the fuzzy affiliation theory. For practicality, the total fuel cost, active power loss, emissions and voltage deviation are selected as objective functions. Due to the limitations of the actual power system, the valve point effect is also considered. The IEEE30-, 57- and IEEE118-bus test systems are used to verify the performance of the proposed MHFPA. In addition, two performance indicators, Hypervolume (HV) and Spacing (SP), quantitatively evaluate the diversity and uniformity of the POS obtained by MHFPA. The simulation results show that, compared with the classic MOPSO and NSGA-II algorithms, the method proposed in this paper shows a greater competitive advantage in dealing with different scales and non-convex optimization problems. [ABSTRACT FROM AUTHOR]

The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems. [ABSTRACT FROM AUTHOR]

A major problem in power systems is achieving a match between the load demand and generation demand, where security, dependability, and quality are critical factors that need to be provided to power producers. This paper proposes a proportional–integral–derivative (PID) controller that is optimally designed using a novel artificial rabbits algorithm (ARA) for load frequency control (LFC) in multi-area power systems (MAPSs) of two-area non-reheat thermal systems. The PID controller incorporates a filter with such a derivative coefficient to reduce the effects of the accompanied noise. In this regard, single objective function is assessed based on time-domain simulation to minimize the integral time-multiplied absolute error (ITAE). The proposed ARA adjusts the PID settings to their best potential considering three dissimilar test cases with different sets of disturbances, and the results from the designed PID controller based on the ARA are compared with various published techniques, including particle swarm optimization (PSO), differential evolution (DE), JAYA optimizer, and self-adaptive multi-population elitist (SAMPE) JAYA. The comparisons show that the PID controller's design, which is based on the ARA, handles the load frequency regulation in MAPSs for the ITAE minimizations with significant effectiveness and success where the statistical analysis confirms its superiority. Considering the load change in area 1, the proposed ARA can acquire significant percentage improvements in the ITAE values of 1.949%, 3.455%, 2.077% and 1.949%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering the load change in area 2, the proposed ARA can acquire significant percentage improvements in the ITAE values of 7.587%, 8.038%, 3.322% and 2.066%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering simultaneous load changes in areas 1 and 2, the proposed ARA can acquire significant improvements in the ITAE values of 60.89%, 38.13%, 55.29% and 17.97%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. [ABSTRACT FROM AUTHOR]

Many real-world engineering problems need to balance different objectives and can be formatted as multi-objective optimization problem. An effective multi-objective algorithm can achieve a set of optimal solutions that can make a tradeoff between different objectives, which is valuable to further explore and design. In this paper, an improved multi-objective differential evolution algorithm (MOEA/D/DEM) based on a decomposition strategy is proposed to improve the performance of differential evolution algorithm for practical multi-objective nutrition decision problems. Firstly, considering the neighborhood characteristic, a neighbor intimacy factor is designed in the search process for enhancing the diversity of the population, then a new Gaussian mutation strategy with variable step size is proposed to reduce the probability of escaping local optimum area and improve the local search ability. Finally, the proposed algorithm is tested by classic test problems (DTLZ1-7 and WFG1-9) and applied to the multi-objective nutrition decision problems, compared to the other reported multi-objective algorithms, the proposed algorithm has a better search capability and obtained competitive results. [ABSTRACT FROM AUTHOR]

Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with non-dominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance. To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms. [ABSTRACT FROM AUTHOR]

In multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to the same lower-level problem. It may lead to poor optimisation results. Therefore, the lower-level search should search lower-level non-dominated solutions that are also non-dominated in the upper-level objective space. In this paper, we use two populations in the lower-level search. The first population maintains non-dominance and diversity in the lower-level objective space and provides the second population with convergence pressure from the lower level. The second population selects the upper-level non-dominated solutions that are not dominated by the first population in the lower-level objective space, which make the second population maintain the non-dominance at both upper and lower levels. Besides, to improve the search efficiency, we set up the upper-level mating pool to generate the upper-level vectors of offsprings near the upper-level vectors of the better individuals in the current population. To balance convergence and diversity, the selection operator of a decomposition based multi-objective evolutionary algorithm is adopted. The proposed algorithm has been evaluated on a set of benchmark problems and a real-world optimisation problem. Experimental results demonstrate that the proposed algorithm is efficient and effective. [ABSTRACT FROM AUTHOR]

A recloser requires a fast operating time in the first shot to optimally clear a temporary fault. The operating time is dependent on the time-dial, the pick-up settings, and the fault current. The recloser detects the fault current from the grid supply; however, the connection of the generators in the distribution system can contribute to the fault current. Depending on the location of the generators and the direction of the current, the fault current can decrease and cause an increase in the operating time. Therefore, the optimal settings that can minimize the operating time may need to be determined. This paper simulates the behavior of a recloser in its first shot for clearing a temporary fault and tests its performance in an active distribution system that has two types of distributed generators. It then uses the differential evolution algorithm to find the optimal settings in the active distribution voltage conditions. It also applies modifications to the differential evolution algorithm and uses these modifications to find robust settings. It then uses an exponential scale factor to balance the exploration and exploitation of the algorithm chosen. Simscape power systems in Matlab Simulink is used to construct the active distribution system and simulate the cases, while the Matlab script is used to run the code for the differential evolution algorithm. Six cases are performed to find the optimal settings of the recloser. The results show that the selected settings and the differential evolution algorithm modification can optimize the operation of the recloser. [ABSTRACT FROM AUTHOR]

Under the too short arc scenario, the evolutionary-based algorithm has more potential than traditional methods in initial orbit determination. However, the underlying multimodal phenomenon in initial orbit determination is ignored by current works. In this paper, we propose a new enhanced differential evolution (DE) algorithm with multimodal property to study the angle-only IOD problem. Specifically, a coarse-to-fine convergence detector is implemented, based on the Boltzmann Entropy, to determine the evolutionary phase of the population, which lays the basis of the balance between the exploration and exploitation ability. A two-layer niching technique clusters the individuals to form promising niches after each convergence detected. The candidate optima from resulting niches are saved as supporting individuals into an external archive for diversifying the population, and a local search within the archive is performed to refine the solutions. In terms of performance validation, the proposed multimodal differential evolution algorithm is evaluated on the CEC2013 multimodal benchmark problems, and it achieved competitive results compared to 11 state-of-the-art algorithms, which present its capability of multimodal optimization. Moreover, several IOD experiments and analyses are carried out on three simulated scenarios of space-based observation. The findings show that, compared to traditional IOD approaches and EA-based IOD algorithms, the proposed algorithm is more successful at finding plausible solutions while improving IOD accuracy. [ABSTRACT FROM AUTHOR]

The quality of operation of neural networks in solving application problems is determined by the success of the stage of their training. The task of learning neural networks is a complex optimization task. Traditional learning algorithms have a number of disadvantages, such as «sticking» in local minimums and a low convergence rate. Modern approaches are based on solving the problems of adjusting the weights of neural networks using metaheuristic algorithms. Therefore, the problem of selecting the optimal set of values of algorithm parameters is important for solving application problems with symmetry properties. This paper studies the application of a new metaheuristic optimization algorithm for weights adjustment—the algorithm of the spiders-cycle, developed by the authors of this article. The approbation of the proposed approach is carried out to adjust the weights of recurrent neural networks used to solve the time series forecasting problem on the example of three different datasets. The results are compared with the results of neural networks trained by the algorithm of the reverse propagation of the error, as well as three other metaheuristic algorithms: particle swarm optimization, bats, and differential evolution. As performance criteria for the comparison of algorithms of global optimization, in this work, descriptive statistics for metrics of the estimation of quality of predictive models, as well as the number of calculations of the target function, are used. The values of the MSE and MAE metrics on the studied datasets were obtained by adjusting the weights of the neural networks using the cycling spider algorithm at 1.32, 25.48, 8.34 and 0.38, 2.18, 1.36, respectively. Compared to the inverse error propagation algorithm, the cycling spider algorithm reduced the value of the error metrics. According to the results of the study, it is concluded that the developed algorithm showed high results and, in the assessment of performance, was not inferior to the existing algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an energy-saving strategy that was applied to a parallel variable frequency pump system of a water circulation pumping station. Firstly, the mathematical model of shaft power consumption for the parallel pump system was established using quadratic polynomial fitting, with some constraints configured according to the system's water supply demands. Then, the algorithm program was designed with the goal of minimizing the energy consumption through the application of an improved differential evolution algorithm. Additionally, the energy consumption model and constraints were integrated and simplified in order to adapt to the algorithm calculation. In the end, the algorithm was implemented according to the pump design parameters and supply targets of the pumping station. Meanwhile, a comparison was done between the differential evolution (DE) algorithm and the genetic algorithm (GA). Furthermore, an experimental test was conducted in an aluminum company in order to verify the applicability of the energy-saving algorithm in practice. The results demonstrated the feasibility of using the improved differential evolution algorithm in order to achieve a minimum energy consumption operation strategy; the results consequently manifested the superiority of the differential evolution algorithm in both computing time and optimal solution aspects. [ABSTRACT FROM AUTHOR]