Abstract Tasmanian devil optimization (TDO) algorithm represents one of the most recent optimization algorithms that were introduced based on the nature behavior of Tasmanian devil behavior. However, as a recent optimizer, its performance may provide inadequate balance among the exploitation and exploration abilities, especially when dealing with the multimodal and high-dimensional natures of optimization tasks. To overcome this shortage, a novel variant of the TDO, called improved Tasmanian devil optimization (ITDO), is introduced in this paper. In ITDO, two competitive strategies are embedded into TDO to enrich the scope of the searching capability with the aim of improving the diversification and identification of the algorithm. The effectiveness of the ITDO algorithm is examined by validating its performance on CEC 2020 benchmark functions with different landscape natures. The recorded results proved that the ITDO is very competitive with other counterparts. After ITDO exhibited a sufficient performance, then, it was applied to estimate the parameters of the 1 kVA, 230/230 V, single-phase transformer. Some assessment metrics along with convergence analysis are conducted to affirm the performance of the proposed algorithm. The recorded results confirm the competitive performance of the proposed method in comparison with the other optimization methods for the benchmark functions and can identify the accurate parameters for the single-phase transformer as the estimated parameters by ITDO are highly coincident with the experimental parameters.

Abstract In this paper, a hybrid algorithm called rough sine cosine algorithm (RSCA) is introduced for solving engineering optimization problems by merging the sine cosine algorithm (SCA) with the rough set theory concepts (RST). RSCA combines the benefits of SCA and RST to focus the search for a promising region where the global solution can be found. Due to imprecise information on the optimization problems, efficient algorithms roughly identify the optimal solution for this type of uncertain data. The fundamental motive for adding the RST is to deal with the imprecision and roughness of the available information regarding the global optimal, especially for large dimensional problems. The cut concept of RST targeted the more interesting search region so the optimal operation could be sped up, and the global optimum could be reached at a low computational cost. The proposed RSCA algorithm is tested on 23 benchmark functions and 3 design problems. RSCA’s obtained results are mainly compared to the SCA, which is used as a first level of the proposed algorithm in this work and those of other algorithms in the literature. According to the comparisons, the RSCA can provide very competitive performance with different algorithms.

Abstract The accuracy of the extracted parameters is important for studying the polyphase induction motor performance and/or the motor control schemes. An investigated and improved interior search algorithm (IISA) is presented in this study for extracting the optimal values of estimated parameters of six-phase and three-phase induction motors. This investigation was carried out on two polyphase induction motors as experimental research cases, utilizing features of manufacturer's operation. The estimated parameters show the high capability regarding the performance of the desired IISA optimizer. The performance of the proposed IISA is compared with different modern optimization algorithms including the basic ISA, and other state-of-the-art approaches. Experimental verifications are validated on two polyphase induction motors, called six-phase and three-phase induction motors. The obtained results show that the proposed method is very competitive in extracting the unknown parameters of different induction motor models with a high degree of closeness to the experimental records. Moreover, various statistical tests, such as the Wilcoxon rank test, stability analysis, and convergence analysis, have been conducted to justify the performance of the proposed IISA. From all the analyses, it has been revealed that the proposed IISA is a competitive method compared to other popular state-of-the-art competitors and ISA variant with accurately identified parameters.

Abstract Parameters identification of Electric Power Transformer (EPT) models is significant for the steady and consistent operation of the power systems. The nonlinear and multimodal natures of EPT models make it challenging to optimally estimate the EPT’s parameters. Therefore, this work presents an improved Dwarf Mongoose Optimization Algorithm (IDMOA) to identify unknown parameters of the EPT model (1-phase transformer) and to appraise transformer aging trend under hottest temperatures. The IDMOA employs a population of solutions to get as much information as possible within the search space through generating different solution’ vectors. Furthermore, the Nelder–Mead Simplex method is incorporated to efficiently promote the neighborhood searching with the aim to find a high-quality solution during the iterative process. At initial stage, power transformer electrical equivalent extraction parameters are expressed in terms of the fitness function and its corresponding operating inequality restrictions. In this sense, the sum of absolute errors (SAEs) among numerous factors from nameplate data of transformers is to be minimized. The proposed IDMOA is demonstrated on two transformer ratings as 4 kVA and 15 kVA, respectively. Moreover, the outcomes of the IDMOA are compared with other recent challenging optimization methods. It can be realized that the lowest minimum values of SAEs compared to the others which are 3.3512e−2 and 1.1200e−5 for 15 kVA and 4 kVA cases, respectively. For more assessment for the proposed optimizer, the extracted parameters are utilized to evaluate the transformer aging considering the transformer hottest temperature compared with effect of the actual parameters following the IEEE Std C57.91 procedures. It is proved that the results are guaranteed, and the transformer per unit nominal life is 1.00 at less than 110 °C as per the later-mentioned standard.

Abstract Climate change and environmental protection have a significant impact on thermal plants. So, the main principles of combined economic-emission dispatch (CEED) problem are indeed to reduce greenhouse gas emissions and fuel costs. Many approaches have demonstrated their efficacy in addressing CEED problem. However, designing a robust algorithm capable of achieving the Pareto optimal solutions under its multimodality and non-convexity natures caused by valve ripple effects is a true challenge. In this paper, chaos-enhanced multi-objective tunicate swarm algorithm (CMOTSA) for CEED problem. To promote the exploration and exploitation abilities of the basic tunicate swarm algorithm (TSA), an exponential strategy based on chaotic logistic map (ESCL) is incorporated. Based on ESCL in CMOTSA, it can improve the possibility of diversification feature to search different areas within the solution space, and then, gradually with the progress of iterative process it converts to emphasize the intensification ability. The efficacy of CMOTSA is approved by applying it to some of multi-objective benchmarking functions which have different Pareto front characteristics including convex, discrete, and non-convex. The inverted generational distance (IGD) and generational distance (GD) are employed to assess the robustness and the good quality of CMOTSA against some successful algorithms. Additionally, the computational time is evaluated, the CMOTSA consumes less time for most functions. The CMOTSA is applied to one of the practical engineering problems such as combined economic and emission dispatch (CEED) with including the valve ripples. By using three different systems (IEEE 30-bus with 6 generators system, 10 units system and IEEE 118-bus with 14 generating units), the methodology validation is made. It can be stated for the large-scale case of 118-bus systems that the results of the CMOTSA are equal to 8741.3 $/h for the minimum cost and 2747.6 ton/h for the minimum emission which are very viable to others. It can be pointed out that the cropped results of the proposed CMOTSA based methodology as an efficient tool for CEED is proven.