Intuitionistic fuzzy-based TOPSIS method for multi-criterion optimization problem: a novel compromise methodology

The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles

On a novel hybrid Manta ray foraging optimizer and its application on parameters estimation of lithium-ion battery

In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon's test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.Web of Science151art. no. 6

Topological optimization of an offshore-wind-farm power collection system based on a hybrid optimization methodology

This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optimization model was formulated, which integrates cable investment, energy loss and line construction. Then, the Prim algorithm was used to initialize the population. A novel hybrid optimization, named PSAO, based on the merits of the particle swarm optimization (PSO) and aquila optimization (AO) algorithms, was presented for topological structure optimization, in which the searching characteristics between PSO and AO are exploited to intensify the searching capability. Lastly, the proposed PSAO method was validated with a real case. The results showed that compared with GA, AO and PSO algorithms, the PSAO algorithm reduced the total cost by 4.8%, 3.3% and 2.6%, respectively, while achieving better optimization efficiency.Web of Science112art. no. 27

A Novel Binary Hybrid PSO-EO Algorithm for Cryptanalysis of Internal State of RC4 Cipher

Cryptography protects privacy and confidentiality. So, it is necessary to guarantee that the ciphers used are secure and cryptanalysis-resistant. In this paper, a new state recovery attack against the RC4 stream cipher is revealed. A plaintext attack is used in which the attacker has both the plaintext and the ciphertext, so they can calculate the keystream and reveal the cipher’s internal state. To increase the quality of answers to practical and recent real-world global optimization difficulties, researchers are increasingly combining two or more variations. PSO and EO are combined in a hybrid PSOEO in an uncertain environment. We may also convert this method to its binary form to cryptanalyze the internal state of the RC4 cipher. When solving the cryptanalysis issue with HBPSOEO, we discover that it is more accurate and quicker than utilizing both PSO and EO independently. Experiments reveal that our proposed fitness function, in combination with HBPSOEO, requires checking 104 possible internal states; however, brute force attacks require checking 2128 states

A hybrid chameleon swarm algorithm with superiority of feasible solutions for optimal combined heat and power economic dispatch problem

Combined heat and power economic dispatch (CHPED) is a challenging important optimization task in the economic operation of power systems that aims to minimize the production cost by scheduling the generation and heat outputs to committed units. The interdependency of heat and power production of the CHPED task exhibits non-convexity and non-linear natures in its modeling and optimization. Therefore, this paper introduces a novel hybrid approach comprising chameleon swarm algorithm (CSA) and mayfly optimization (MO), named CSMO, for solving the CHPED problem. The proposed CSMO algorithm has a better capability to evade from the trapping in local optima with faster rate of convergence pattern than the traditional CSA. Also the proposed CSMO algorithm employs the MO' phase to assist the CSA to search based on deeper exploration/exploitation capabilities as MO utilizes two populations of male and female mayflies with crossover-based matting process. The effectiveness of the proposed CSMO algorithm is validated on CEC 2017 benchmark functions and two systems of the CHPED problem. The obtained results are compared with some successful optimizers. The simulation outcomes are portrayed based on the number of occasions where CSMO performs superior/equal/inferior to the other optimizers by considering the smaller mean values obtained by each algorithm for all test suites. Accordingly, it is exposed that the occasions achieved by the proposed CSMO are 29/1/0, 30/0/0, 30/0/0, 28/2/0, and 30/0/ 0 against some implemented algorithms, i.e., ISA, GOA, GBO, EO, and the original CSA. Similarly, the number of occasions achieved by the proposed CSMO are 30/0/0, 30/0/0, 30/0/0, 30/0/0, 30/0/0, 29/1/0, and 22/2/6 when the simulations are portrayed against some competitors from literature including the PSO, FA, FFPSO, HPSOFF, HFPSO, HGSO, and Q-SCA, respectively. Furthermore, the results of total cost found by CSMO are 9257.07 $/h for system 1 and 10094.25$/h for system 2 of the CHPED problem, with percentage of improvement 0.02% and 14.42% on the original CSA, respectively. In addition, further assessments based on the Wilcoxon test, and convergence characteristic are reported. Based on the recorded results, it is portrayed that the CSMO can efficiently deal with the CEC 2017 benchmark functions and CHPED problem.Web of Science254art. no. 12434

A hybrid reptile search algorithm and Levenberg–Marquardt algorithm based Haar wavelets to solve regular and singular boundary value problems

Various engineering applications lead to the appearance of partial differential equations resulting in boundary value problems (BVPs). Orthogonal collocation method based Haar wavelets has gained significant attention in solving these problems. The Haar wavelets have several properties like vanishing moment, compact effect, and orthogonality. These properties prioritize being used as base functions for solving BVPs. However, the approximation leads to a relatively high number of unknown coefficients, which needs an efficient and reliable nonlinear solver to reach their values. The Levenberg–Marquardt algorithm (LM) is one of the most efficient nonlinear solvers. However, it may diverge in case of too far initial guesses, especially in many unknowns. The reptile search algorithm (RSA) is a recent reliable, nature-inspired optimization technique that has a noticeable capability in dealing with high-dimensional issues. Therefore, this paper proposes a hybrid optimization algorithm that integrates the RSA and LM algorithms using Haar wavelets as bases, named RSA–LM–Haar algorithm, to solve regular and singular natures of the BVPs more efficiently. To evaluate the performance of the proposed RSA–LM–Haar algorithm, it is tested on twelve case studies of BVPs including the singular and regular problems. The results are compared with those based on the LM alone, named LM–Haar algorithm. Finally, the applicability of the proposed algorithm is verified using two practical chemical applications to prove its ability to solve real-time applications effectively. All the results affirmed the capability of the proposed algorithm in solving both regular and singular BVPs. All results and comparisons illustrated that the proposed hybridization algorithm provides remarkable performance.Web of Science6041823179

Guided golden jackal optimization using elite-opposition strategy for efficient design of multi-objective engineering problems

Multi-objective optimization (MOO) issues that are encountered in the realm of real engineering applications are characterized by the curse of economically or computationally expensive objectives, which can strike insufficient performance evaluations for optimization methods to converge to Pareto optimal front (POF). To address these concerns, this paper develops a guided multi-objective golden jackal optimization (MOGJO) to promote the coverage and convergence capabilities toward the true POF while solving MOO issues. MOGJO embeds four reproduction stages during the seeking process. Firstly, the population of golden jackals is initialized according to the operational search space and then the updating process is performed. Secondly, an opposition-based learning scheme is adopted to improve the coverage of the Pareto optimal solutions. Thirdly, an elite-based guiding strategy is incorporated to guide the leader golden jackal toward the promising areas within the search space and then promote the convergence propensity. Finally, the crowding distance is also integrated to provide a better compromise among the diversity and convergence of the searched POF. To evaluate the MOGJO’s performance, it is analyzed against sixteen frequently utilized unconstrained MOO issues, five complex constrained problems, four constrained engineering designs, and real dynamic economic-emission power dispatch (DEEPD) problem. The experimental results are performed using the generational distance (GD), hypervolume (HV), spacing (SP) metrics to validate the efficacy of the proposed methods, which affirms the progressive and competitive performance compared to thirteen state-of-the-art methods. Finally, the results of the Wilcoxon rank sum test with reference to GD and HV exhibited that the proposed algorithm is significantly better than the compared methods, with a 95% significance level. Furthermore, the results of the nonparametric Friedman test were performed to detect the significant of average ranking among the compared algorithm, where the results confirmed that the proposed MOGJO outperforms the best algorithm among thirteen state-of-the-art algorithms by an average rank of Friedman test greater than 41% while outperforming the worst one, MOALO, by 84% for ZDT and DTLZ1 suits. Additionally, the proposed algorithm saved the overall energy cost and total emission of the DEEPD problem by 1.89%, and 1.48%, respectively, compared with the best existing results and thus, it is commended to adopt for new applications.Web of Scienc

Weighted mean of vectors optimization algorithm and its application in designing the power system stabilizer

Accurate design of the power system stabilizer (PSS) models is a crucial issue due to their significant impact on the stability of power system operation. However, identifying the parameters of a PSS model is a challenging task owing to its nonlinearity and multi-modality characteristics. Due to such characteristics, handling algorithms may be prone to stagnation in local optima. Therefore, this paper proposes a potent integrated optimization algorithm by comprising the weIghted meaN oF vectOrs (INFO) optimizer with chaotic-orthogonal based learning (COBL) and Gaussian bare-bones (GBB) strategies, named INFO-GBB, for achieving the optimal parameters of a PSS model used in a single-machine infinite-bus (SMIB) system. In the INFO-GBB, the COBL aims to enhance the searching capability to explore new regions using the orthogonal design aspect and thus improving the diversity of solutions. Also, the GBB is adopted to assist the algorithm to perform an immediate vicinity of the best solution and thus enhances the exploitation capabilities. The effectiveness and efficacy of the INFO-GBB algorithm is validated on CEC 2020 benchmark suits and the designing task of the PSS model. The achieved results by the INFO-GBB are compared with eighteen well-known algorithms. The statistical verifications along with the Friedman test have ascertained that the INFO-GBB is capable of achieving promising performances compared to the other counterparts. The results obtained based on the Friedman test illustrate that the INFO-GBB offers superior performance over the state-of-the-art algorithms as it outperforms fifteen out of eighteen algorithms by an average rank greater than 61% for benchmark problems while outperforming O-LSHADE, LSHADE, and TSA algorithms by 25%,33%, and 58%, respectively. Furthermore, the applicability of the INFO-GBB is realized through designing the PSS model used in a SMIB system. The obtained results indicate that the INFO-GBB algorithm exhibits accurate and superior performance compared to other peers as it provides the lowest value for the integral of time multiplied absolute error (ITAE) performance index which is used as an objective function. For example, the achieved results of the mean ITAE found by INFO-GBB is 1.36E−03 with improvement percentages of 24.93%, 19.78%, 13.04%, 26.64%, and 24.86%, over the LSHADE, GWO, EO, RSA, and original INFO algorithms, respectively. Therefore, the INFO-GBB can efficiently affirm its superiority and stability to deal with the function optimization task and parameters’ estimation of the PSS model.Web of Science136art. no. 11008