Symbolization-based differential evolution strategy for identification of structural parameters

The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.

Particle Swarm Optimization or Differential Evolution—A comparison

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.

In swarm intelligence, Particle Swarm Optimization (PSO) and Differential Evolution (DE) have been successfully applied in many optimization tasks, and a large number of variants, where novel algorithm operators or components are implemented, has been introduced to boost the empirical performance. In this paper, we first propose to combine the variants of PSO or DE by modularizing each algorithm and incorporating the variants thereof as different options of the corresponding modules. Then, considering the similarity between the inner workings of PSO and DE, we hybridize the algorithms by creating two populations with variation operators of PSO and DE respectively, and selecting individuals from those two populations. The resulting novel hybridization, called PSODE, encompasses most up-to-date variants from both sides, and more importantly gives rise to an enormous number of unseen swarm algorithms via different instantiations of the modules therein. In detail, we consider 16 different variation operators originating from existing PSO- and DE algorithms, which, combined with 4 different selection operators, allow the hybridization framework to generate 800 novel algorithms. The resulting set of hybrid algorithms, along with the combined 30 PSO- and DE algorithms that can be generated with the considered operators, is tested on the 24 problems from the well-known COCO/BBOB benchmark suite, across multiple function groups and dimensionalities.

SUMMARYIn this paper, we present a new type of hybrid methods for global optimization with particle swarm optimization (PSO) and differential evolution (DE), which have been attracting interest as heuristic and global optimization methods. Concretely, “p‐best solutions” as the targets of PSO's particles are actuated by DE's evolutional mechanism in order to promote PSO's global searching ability. The presented hybrid method works effectively because PSO acts as a local optimizer and DE plays a role as a global optimizer. To evaluate performance of the hybridization, our method is applied to some benchmarks and is compared with the separated PSO and DE. Through computer simulations, it is demonstrated that the proposed hybrid method performs rather better than the component algorithms separately.

Evaluation of stochastic global optimization methods for modeling vapor–liquid equilibrium data

This paper presents a novel fuzzy modeling strategy based on the hybrid of particle swarm optimization (PSO) and differential evolution (DE), and the proposed hybrid algorithm is referred to as PSODE. PSODE is based on a two-population scheme, in which the individuals in one population is enhanced by PSO and the individuals in the other population is evolved by DE. The individuals both in PSO and DE are co-evolved during the algorithm execution by employing an information sharing mechanisms. To further improve the proposed PSODE algorithm a nonlinear inertia weight approach and a mutation mechanism are presented respectively. In the simulation part, the PSODE is used to automatic design of fuzzy identifier for a nonlinear dynamic system. The performance of the suggested method is compared to PSO, DE and some other methods in the fuzzy identifier design to demonstrate its superiority.

Classification has been widely studied due to its practical applications. Feature selection aims to improve the classification accuracy by selecting a small feature subset from the original full feature set. However, identification of relevant features is not trivial due to the large search space. Particle swarm optimisation (PSO) is an efficient meta-heuristic algorithm which has shown to be promising in feature selection. However, traditional PSO uses its personal best experience and its historical best experience to determine its search direction, but this learning strategy may limit its performance for feature selection due to the premature convergence. Therefore, the potential of PSO needs to be further explored. In this paper, a new evolutionary learning algorithm termed hybridising PSO with differential evolution (HPSO-DE) is proposed to develop new feature selection methods. In HPSO-DE, differential evolution is applied to breed promising and efficient exemplars for PSO to guide its search, which is expected to not only preserve the diversity of the population but also guide particles to fly to promising areas. HPSO-DE is compared with three classic PSO variants and five traditional feature selection methods on 15 classification problems. The results show that the proposed algorithm can effectively achieve a higher classification accuracy with a smaller feature subset than the compared methods.

Real-life optimization problems (RLOPs) are common in numerous research disciplines. In literature, evolutionary algorithms (EAs) have been deemed an emerging field to solve RLOPs. Among popular EAs, Differential Evolution (DE) is clever algorithm to solve RLOPs. DE and its varied alternatives are highly influenced by improper operators like mutation and crossover. Mostly, DE doesn't compel to learn the optimum properties achieved in primary phase of the former aristocrats. In this article, a new DE (named as <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mb</sup> DE) created on memory mechanism is offered to solve a renowned RLOP, specifically speed reducer design. It enclosed different crossover & mutation (swarm, crossover and mutation) made through particle swarm optimization (PSO) context. Experimental outcomes endorse the competency of <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mb</sup> DE over various procedures.

Fuel cells are complex multiphysical systems characterized by a stratified geometry consisting of thin layers of different materials. Their full characterization involves a number of physical parameters, which are important for quantifying and comparing the performance of the materials. The physical parameters are also used in fuel cell models, which allow the fast exploration of different operating scenarios and can be implemented in optimization procedures aimed at achieving the best structural and operating conditions. The system of equations involved in these models, e.g. the Nernst equation, the Butler-Volmer equation and the diffusion equation, are strongly non-linear; thus, the model itself is extremely sensitive to parameter variations. Careful ex-situ measurements of such parameters can be performed by means of a number of diagnostic techniques, whose results are however not completely consistent with fuel cell operation due to several effects, e.g. variable interactions and boundary effects in the thin layers of the fuel cell. Conversely, “in situ” measurements can provide meaningful operational values, but a very few techniques are available to determine a limited number of parameters.A solution can consist in multiple parameter identification from multiple fundamental measurements, such as polarization curve and electrochemical impedance spectroscopy, performed in different conditions, e.g. at different temperature, pressure, concentration, and humidification.If there is only one unknown parameter, the solution is easy, requiring just a statistical interpolating technique. In the case of multiple unknown parameters, which is the most frequent condition, the problem becomes increasingly difficult as the number of parameters increases as duplicity problem emerges, i.e., several groups of parameters may lead to the same performance (e.g. polarization curve).Recently, a number of numerical tools are under development to face this kind of problems, some based on stochastic mathematical models, each presenting specific features in terms of accuracy, convergence, stability, robustness, and calculation speed [1]. Among the most promising methods, some of which have already been applied to multiple fuel cell parameter identification, are the Ishibashi–Kimura method [2], the Lambert W-function method [3], the Levenberg-Marquardt algorithm (LMA), the Gauss-Newton algorithm (GNA), the hybrid artificial bee colony algorithm [4], the trust region method, the current switching method [5], the simulated annealing method [6], and the particle swarm optimization (PSO) [7], and the differential evolution method [8].The presentation will focus on the application of a particular class of stochastic optimization methods to the problem of multiple parameters identification. These methods have already been widely tested and applied successfully to the simultaneous search of sets of optimal parameters with different physical dimensions, making them ideal tools to cope with the present problem. Their recent relevant literature is vast. They are especially suited for multimodal and noisy functions over large search spaces, which makes them particularly appealing for the problem at hand. The main drawback of such methods is the high number of objective function evaluations needed for convergence; however, thanks to a particular formulation of the PEMFC model, such drawback can be of minor impact, i.e. the overall computational times are on the order of a few tens of seconds.We are going to present the application of two of the most well-known metaheuristic methods, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE), developed in previous work and suitably adapted. It should be noted that PSO has already been applied to parameter identification for FCs, while the application of DE is novel in this context. This is somewhat surprising since, as results will show, DE provides better results and offers higher robustness. We will present the implementation details regarding PSO and DE. The optimization algorithms have not been tuned to the specific problem and algorithm control parameters have been chosen from reasonable values suggested in literature.

When designing a well field for efficiently extracting groundwater or petroleum, it is common for designers to rely on computational optimization methods to determine the optimal placement of wells. The goal of these methods is to find a well-field solution that maximizes the value of a defined objective function, and to do so while utilizing the least amount of computational effort. To achieve this, researchers have developed algorithms based on a wide range of heuristics. Within groundwater management, popular methods include particle swarm optimization (PSO) and genetic algorithms such as differential evolution (DE). This study seeks to investigate a recently developed method called Extremal Optimization for Well Placement Problems (EO-WPP), and to compare its performance with established methods like PSO and DE. EO-WPP is an optimization method based on the extremal optimization (EO) algorithm. EO optimizes by iteratively identifying and modifying the least effective components of a solution. By following this heuristic, the EO algorithm has the potential to quickly find optimal solutions while requiring minimal computational effort. To test this, the performance of DE, PSO and EO-WPP was compared on four benchmark problems. Two of these are the Rastrigin and the Rosenbrock benchmark functions. These functions were used because of their quick evaluation and their popularity in optimization literature. The third benchmark is a synthetic groundwater model, built to test the methods under the context of groundwater management. The final benchmark is a field problem using the Aberdeen groundwater model in South Dakota. The results reveal that EO-WPP was able to outperform DE and PSO on all tested benchmarks. EO-WPP is an effective and efficient optimization tool for well placement design in groundwater management.

A heuristic method for the supplier selection and order quantity allocation problem

To improve the performance of particle swarm optimization and differential evolution, the diploid differential evolution particle swarm optimization was presented according to the genetics rules. The algorithm was applied into the Open Vehicle Routing Problem. In the algorithm, dominant character and recessive character were included in every individual. Particle swarm optimization was implemented by dominant character, and differential evolution was implemented by recessive character. Dominant character was replaced by recessive character if its fitness was smaller than recessive character. A real number encoding method was presented and some heuristics were modified and improved the solution. The parameters of algorithm were analyzed in the experiment. And comparison with other algorithms in the literatures showed the proposed algorithm was effective.

This paper presents a hybrid optimization strategy for wideband antenna design that leverages the strengths of both Random Forest (RF) and Differential Evolution (DE) algorithms. The strategy employs DE for iteratively updating antenna parameters and RF for feature selection in the process of antenna performance optimization. Initially, DE is applied to update antenna parameters for a predetermined number of iterations, generating a dataset of antenna performance metrics. This dataset is then used to train an RF model, which identifies the importance of each design variable. Feature selection, guided by the RF-derived importance, is applied to reduce the dimensionality of the search space. DE subsequently continues the optimization process within this reduced parameter space. Validation of this hybrid approach is performed through the design of a wideband slot antenna and compared against standalone DE, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). Results demonstrate that the proposed strategy significantly accelerates convergence, achieving the target reflection coefficient and gain with substantially fewer iterations than the other methods (reductions of 75.56%, 45%, 42.11%, and 50% compared to DE, GA, SA, and PSO, respectively). Furthermore, the hybrid strategy consistently finds superior solutions exhibiting lower loss values compared to the benchmark algorithms. The method offers a computationally efficient, interpretable, and effective approach to antenna optimization.

In the event of a fault isolation process, all loads located downstream from the faulted point become out of service, and as a consequence, the power interruption affects a greater portion of the radial distribution system. This paper proposes an optimal Service Restoration (SR) method that entails changing the network topology configuration via optimal tie-switch and section switch combinations. However, when the network topology configuration is performed, it results in increased load currents. As a result, some Protective Devices (PDs) can operate undesirably and some network branches may become unprotected. Therefore, it is essential to consider protection constraints in the SR problem to maintain service continuity during power interruptions. The proposed method aims at optimal SR with minimum out-of-service loads, minimum power loss, and improved voltage profiles and at the same time ensures PDs operate correctly during the normal and overloading conditions. The proposed method was carried out on the Debre Markos distribution networks, using the Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO), and Differential Evolutionary (DEV) algorithms. The proposed SR was carried out considering and without considering protection constraints. The obtained SR topology was not feasible for SR without considering protection constraints, since some PDs fail to operate properly in normal loading conditions. After executing the proposed SR algorithms by considering protection constraints for a single fault case, the power loss reductions in TLBO, DEV, and PSO were 64.9073%, 45.9073%, and 55.358 %, respectively. The minimum voltage profiles obtained in each proposed TLBO, DEV, and PSO algorithm were 0.96%, 0.95%, and 0.96%, respectively. In each algorithm, except for the branch under fault, all healthy out-of-service branches were restored. When the protection constraints were considered in an optimal SR, load current did not exceed the rating of the fuses. The results show the importance of considering protection constraints during SR to prevent dysfunction of the PDs in the network. Comparative analyses were carried out on each algorithm and TLBO algorithms performed better than PSO and DEV for search functions.