Balancing diversity and convergence among solutions in many-objective optimization is challenging, particularly in high-dimensional spaces with conflicting objectives. This paper presents the Many-Objective Marine Predator Algorithm (MaOMPA), an adaptation of the Marine Predators Algorithm (MPA) specifically enhanced for many-objective optimization tasks. MaOMPA integrates an elitist, non-dominated sorting and crowding distance mechanism to maintain a well-distributed set of solutions on the Pareto front. MaOMPA improves upon traditional metaheuristic methods by achieving a robust balance between exploration and exploitation using the predator-prey interaction model. The algorithm underwent evaluation on various benchmarks together with complex real-world engineering problems where it showed superior outcomes when compared against state-of-the-art generational distance and hypervolume and coverage metrics. Engineers and researchers can use MaOMPA as an effective reliable tool to address complex optimization scenarios in engineering design. The MaOMPA source code is available at https://github.com/kanak02/MaOMPA .

• Klíčová slova
• Convergence, Diversity, Information feedback mechanism, Many-objective optimization, Marine predator algorithm, Metaheuristic algorithm,
• MeSH
• algoritmy * MeSH
• potravní řetězec * MeSH
• predátorské chování * MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.

• Klíčová slova
• Ant lion optimizer, Convergence, Diversity, MaF benchmark, Many-objective optimization,
• Publikační typ
• časopisecké články MeSH

Several optimization problems can be abstracted into many-objective optimization problems (MaOPs). The key to solving MaOPs is designing an effective algorithm to balance the exploration and exploitation issues. This paper proposes a novel many-objective African vulture optimization algorithm (MaAVOA) that simulating the African vultures' foraging and navigation behaviours to solve the MaOPs. MaAVOA is an updated version of the African Vulture Optimization Algorithm (AVOA), which was recently proposed to solve the MaOPs. A new social leader vulture for the selection process is introduced and integrated into the proposed model. In addition, an environmental selection mechanism based on the alternative pool is adapted to improve the selection process to maintain diversity for approximating different parts of the whole Pareto Front (PF). The best-nondominated solutions are saved in an external Archive based on the Fitness Assignment Method (FAM) during the population evolution. FAM is based on a convergence measure that promotes convergence and a density measure that promotes variety. Also, a Reproduction of Archive Solutions (RAS) procedure is developed to improve the quality of archiving solutions. RAS has been designed to help reach out to the missing areas of the PF that the vultures easily miss. Two experiments are conducted to verify and validate the suggested MaAVOA's performance efficacy. First, MaAVOA was applied to the DTLZ functions, and its performance was compared to that of several popular many-objective algorithms and according to the results, MaAVOA outperforms the competitor algorithms in terms of inverted generational distance and hypervolume performance measures and has a beneficial adaptation ability in terms of both convergence and diversity performance measures. Also, statistical tests are implemented to demonstrate the suggested algorithm's statistical relevance. Second, MaAVOA has been applied to solve two real-life constrained engineering MaOPs applications, namely, the series-parallel system and overspeed protection for gas turbine problems. The experiments show that the suggested algorithm can tackle many-objective real-world applications and provide promising choices for decision-makers.

• MeSH
• algoritmy * MeSH
• počítačová simulace MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm's key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA's performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima.

• Klíčová slova
• optimization, optimization problem, population updating, population-based algorithm, selected variables, stochastic methods,
• Publikační typ
• časopisecké články MeSH

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling. With the above motivation, this article suggests a stage-specific mutation strategy for the proposed enhanced differential evolution (EDE) that adopts a better search process to arrive at optimal solutions by adaptively varying the mutation factor and crossover rate at different search stages. The optimal identification of PV systems is formulated as a single objective function. It appears in the form of the Root Mean Square Error (RMSE) between the PV model current from the experimental data and the current calculated using the identified parameters considering the parameter constraints (limits). The I-V (current-voltage) characteristics/data with identified parameters are validated with the experimental data to justify the proposed approach's accuracy and efficacy for different cells and modules. Extensive simulation has been demonstrated considering two different PV cells (RTC France & PVM-752-GaAs) and three different PV modules (ND-R250A5, STM6 40/36 & STP6 120/36). The results obtained from the proposed EDE technique show Root Mean Square Errors (RMSE) of 7.730062e-4, 7.419648e-4, and 7.33228e-4 respectively, in parameter identification of RTC France PV cell models based on single, double, and triple diodes. Also, the RMSE involved in parameter identification of PVM-752-GaAs PV cell models based on single, double, and triple diodes are 1.59256e-4, 1.408989e-4, and 1.30181e-4, respectively. The parameters identification of ND-R250A5, STM6 40/36 and STP6 120/36 PV modules involve RMSE values of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively. All these RMSE values obtained with proposed EDE are the least as compared to other well-accepted algorithms, thereby justifying its higher accuracy.

• Klíčová slova
• Enhanced differential evolution, Metaheuristic algorithm, Optimization technique, PV model, Parameter identification,
• Publikační typ
• časopisecké články MeSH

Implementing a suitable load frequency controller to maintain the power balance equation for a multi-area system with many power generating units poses a challenge to a power system engineer. Incorporation of renewable energy sources along with non-renewable units is another challenge while maintaining the stability of the system. Hence a robust intelligent controller is an essential requirement to achieve the objective of automatic load frequency control. This article introduces a novel and efficient controller designed for a three-control area within a deregulated multi-source energy system. The three areas include diverse power generation sources: Area 1 integrates thermal units, hydro units, and solar thermal power plants. In Area 2, there is a combination of distributed solar technology (DST) with thermal and hydro units. Area 3 incorporates a geothermal power plant alongside thermal and hydro unit. The proposed controller is a parallel combination of the tilted integral derivative controller (TID) and the integral derivative with a first-order filter effect (IDN). The controller's parameters are optimized using an advanced Coatis Optimization Algorithm (COA). High effective efficiency and absence of control parameters are the key advantages of Coatis Optimization Algorithm. The article highlights the superior performance of the newly developed TID + IDN controller in comparison to standalone TID and IDN controllers. This assessment is based on the observation of dynamic responses across different controller configurations. Additionally, the study examines the system's behaviour when incorporating energy storage units such as Redox Flow Batteries (RFB). Furthermore, the research investigates the system under various power transactions in a deregulated environment, considering generation rate constraints and governor dead bands. The proposed approach's robustness is demonstrated by subjecting it to extensive variations in system parameters and random load fluctuations. In summary, this paper presents an innovative TID + IDN controller optimized using a novel Coatis Optimization Algorithm within a three-area hybrid system operating in a deregulated context. Considering the poolco transaction and implementing the COA optimized TID + IDN controller with an error margin of 0.02%, the value of the objective function, ITAE for the transient responses is 0.1233. This value is less than the value obtained in other controllers optimized with different optimization techniques. In case of poolco transaction, the settling time of deviation of frequency in area-1, deviation of frequency in area-2, and deviation of frequency in area-3 are 8.129, 3.72, and 2.254 respectively. As compared to other controllers, the transient parameters are better in case of this proposed controller.

• Klíčová slova
• Coatis optimization algorithm (COA), Improved squirrel search algorithm (ISSA), Independent system operator (ISO), Integral derivative with a first-order filter effect (IDN), Integral time multiplied by absolute error (ITAE), Load frequency control (LFC), PID, Particle swarm optimization (PSO), Squirrel search algorithm (SSA), Tilted integral derivative controller (TID),
• Publikační typ
• časopisecké články MeSH

In the Czech part of the Upper Silesian Coal Basin (Moravian-Silesian region, Czech Republic), there are many deposits of endogenous combustion (e.g., localized burning soil bodies, landfills containing industrial waste, or slag rocks caused by mining processes). The Hedwig mining dump represents such an example of these sites where, besides the temperature and the concentrations of toxic gases, electric and non-electric quantities are also monitored within the frame of experimentally proposed and patented technology for heat collection (the so-called "Pershing" system). Based on these quantities, this paper deals with the determination and evaluation of negative heat sources and the optimization of the positive heat source dependent on measured temperatures within evaluation points or on a thermal profile. The optimization problem is defined based on a balance of the heat sources in the steady state while searching for a local minimum of the objective function for the heat source. From an implementation point of view, it is the interconnection of the numerical model of the heat collector in COMSOL with a user optimization algorithm in MATLAB using the LiveLink for MATLAB. The results are elaborated in five case studies based on the susceptibility testing of the numerical model by input data from the evaluation points. The tests were focused on the model behavior in terms of preprocessing for measurement data from each chamber of the heat collector and for the estimated value of temperature differences at 90% and 110% of the nominal value. It turned out that the numerical model is more sensitive to the estimates in comparison with the measured data of the chambers, and this finding does not depend on the type optimization algorithm. The validation of the model by the use of the mean-square error led to the finding of optimal value, also valid with respect to the other evaluation.

• Klíčová slova
• COMSOL multiphysics, MATLAB, borehole heat exchanger (BHE), liveLink for MATLAB, optimization, partial differential equation,
• Publikační typ
• časopisecké články MeSH

In wireless sensor networks (WSNs), existing routing protocols mainly consider energy efficiency or security separately. However, these protocols must be more comprehensive because many applications should guarantee security and energy efficiency, simultaneously. Due to the limited energy of sensor nodes, these protocols should make a trade-off between network lifetime and security. This paper proposes a cluster-tree-based trusted routing method using the grasshopper optimization algorithm (GOA) called CTTRG in WSNs. This routing scheme includes a distributed time-variant trust (TVT) model to analyze the behavior of sensor nodes according to three trust criteria, including the black hole, sink hole, and gray hole probability, the wormhole probability, and the flooding probability. Furthermore, CTTRG suggests a GOA-based trusted routing tree (GTRT) to construct secure and stable communication paths between sensor nodes and base station. To evaluate each GTRT, a multi-objective fitness function is designed based on three parameters, namely the distance between cluster heads and their parent node, the trust level, and the energy of cluster heads. The evaluation results prove that CTTRG has a suitable and successful performance in terms of the detection speed of malicious nodes, packet loss rate, and end-to-end delay.

• MeSH
• algoritmy MeSH
• kobylky * MeSH
• komunikace MeSH
• záplavy MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

We deal with two-dimensional copulas from the perspective of their differential entropy. We formulate a problem of finding a copula with maximum differential entropy when some copula values are given. As expected, the solution is a copula with a piecewise constant density (a checkerboard copula). This allows us to simplify the optimization of the continuous objective function, the differential entropy, to an optimization of finitely many density values. We present several ideas to simplify this problem. It has a feasible numerical solution. We also present several instances that admit closed-form solutions.

• Klíčová slova
• convex optimization, copula, density, maximum entropy estimator,
• Publikační typ
• časopisecké články MeSH

Electric train system is a very large load for the power network. This load consumes a large amount of reactive power. In addition, it causes a massive unbalance to the network, which results in many problems such as voltage drops, high transmission losses, reduction in the transformer output ability, negative sequence current, mal-operation of protective relays, etc. In this paper, a novel real-time optimization approach is presented to adjust the static VAR compensator (SVC) for the traction system to realize two objectives; current unbalance reduction and reactive power compensation. A multi-objective optimization technique entitled non-dominated sorting genetic algorithm (NSGA-II) is used to fulfill the regarded objectives simultaneously. A comprehensive simulator has been designed for electric train network modeling that is able to adjust the parameters of SVC in an optimum manner at any time and under any circumstances. The results illustrate that the provided method can efficiently reduce the unbalancing in current as well as supply the demanded reactive power with acceptable precision.

• Klíčová slova
• SVC, current unbalance, multi-objective optimization, reactive power compensation, traction system,
• Publikační typ
• časopisecké články MeSH