This paper proposes a multi-objective Slime Mould Algorithm (MOSMA), a multi-objective variant of the recently-developed Slime Mould Algorithm (SMA) for handling the multi-objective optimization problems in industries. Recently, for handling optimization problems, several meta-heuristic and evolutionary optimization techniques have been suggested for the optimization community. These methods tend to suffer from low-quality solutions when evaluating multi-objective optimization (MOO) problems than addressing the objective functions of identifying Pareto optimal solutions’ accurate estimation and increasing the distribution throughout all objectives. The SMA method follows the logic gained from the oscillation behaviors of slime mould in the laboratory experiments. The SMA algorithm shows a powerful performance compared to other well-established methods, and it is designed by incorporating the optimal food path using the positive-negative feedback system. The proposed MOSMA algorithm employs the same underlying SMA mechanisms for convergence combined with an elitist non-dominated sorting approach to estimate Pareto optimal solutions. As a posteriori method, the multi-objective formulation is maintained in the MOSMA, and a crowding distance operator is utilized to ensure increasing the coverage of optimal solutions across all objectives. To verify and validate the performance of MOSMA, 41 different case studies, including unconstrained, constrained, and real-world engineering design problems are considered. The performance of the MOSMA is compared with Multiobjective Symbiotic-Organism Search (MOSOS), Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multiobjective Water-Cycle Algorithm (MOWCA) in terms of different performance metrics, such as Generational Distance (GD), Inverted Generational Distance (IGD), Maximum Spread (MS), Spacing, and Run-time. The simulation results demonstrated the superiority of the proposed algorithm in realizing high-quality solutions to all multi-objective problems, including linear, nonlinear, continuous, and discrete Pareto optimal front. The results indicate the effectiveness of the proposed algorithm in solving complicated multi-objective problems. This research will be backed up with extra online service and guidance for the paper’s source code at https://premkumarmanoharan.wixsite.com/mysite and https://aliasgharheidari.com/SMA.html . Also, the source code of SMA is shared with the public at https://aliasgharheidari.com/SMA.html .

The primary concerns in the practical photovoltaic (PV) system are the power reduction due to the change in operating conditions, such as the temperature or irradiance, the high computation burden due to the modern maximum power point tracking (MPPT) mechanisms, and to maximize the PV array output during the rapid change in weather conditions. The conventional perturb and observation (P&O) technique is preferred in most of the PV systems. Nevertheless, it undergoes false tracking of maximum power point (MPP) during the rapid change in solar insolation due to the wrong decision in the duty cycle. To avoid the computational burden and drift effect, this article presents a simple and enhanced P&O MPPT technique. The proposed technique is enhanced by including the change in current (dI), in addition to the changes in output voltage and output power of the PV module. The effect of including the dI profile with the traditional method is explained with the fixed and variable step-size methods. The mathematical expression for the drift-free condition is derived. The traditional boost converter is considered for validating the effectiveness of the proposed methods by employing the direct duty cycle technique. The proposed algorithm is simulated using MATLAB/Simulink and validated under various scenarios with the developed laboratory prototype in terms of drift-free characteristics and tracking efficiency. The result proves that the proposed technique can track the MPP accurately under various operating conditions.

In this paper, a new Multi-Objective Arithmetic Optimization Algorithm (MOAOA) is proposed for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such problems can be found in different areas, including mechanical engineering, chemical engineering, process and synthesis, and power electronics systems. MOAOA is inspired by the distribution behavior of the main arithmetic operators in mathematics. The proposed multi-objective version is formulated and developed from the recently introduced single-objective Arithmetic Optimization Algorithm (AOA) through an elitist non-dominance sorting and crowding distance-based mechanism. For the performance evaluation of MOAOA, a set of 35 constrained RWMOPs and five ZDT unconstrained problems are considered. For the fitness and efficiency evaluation of the proposed MOAOA, the results obtained from the MOAOA are compared with four other state-of-the-art multi-objective algorithms. In addition, five performance indicators, such as Hyper-Volume (HV), Spread (SP), Inverse Generalized Distance (IGD), Runtime (RT), and Generalized Distance (GD), are calculated for the rigorous evaluation of the performance and feasibility study of the MOAOA. The findings demonstrate the superiority of the MOAOA over other algorithms with high accuracy and coverage across all objectives. This paper also considers the Wilcoxon signed-rank test (WSRT) for the statistical investigation of the experimental study. The coverage, diversity, computational cost, and convergence behavior achieved by MOAOA show its high efficiency in solving ZDT and RWMOPs problems.

The hybrid model of the power network infrastructure is an essential part of the sophisticated technology of the electrical network. For the traditional Optimal Power Flow (OPF) problem, thermal generators are typically employed to generate electricity. In this case, the amount of fuel required to produce power is constrained, and emissions from the network are frequently neglected. Renewable Energy Sources (RESs) have received increasing attention due to various potential characteristics such as cleanliness, diversity, and renewability. As an outcome, RESs are being integrated into the electrical grid at an increasing rate. The study in this paper proposes a techno-monetary investigation into the single- and multi-objective OPF, coordinating with RESs like wind, PhotoVoltaic (PV), and small hydropower units with hybrid PV. Moreover, the probability density functions of Weibull, Lognormal, and Gumble have been used independently to predict the required power in this paper. This paper considers an equilibrium optimizer for handling the OPF problem. The superior performance of the equilibrium optimizer is further verified with the results of both single- and multi-objective through comparative analysis with state-of-the-art counterparts, and the indications are that the proposed method can find better optimal solutions, use a lower number of generated solutions, and faster convergence with well distributed optimal Pareto front for multi-objective problems. The results are verified by employing a modified IEEE-30 bus hybrid power network, and performance comparisons are made among several well-established algorithms. Simulation findings show that the suggested strategy can achieve a reasonable compromise solution for different objectives.

The photovoltaic (PV) systems must work at the maximum power point (MPP) to derive the highest possible power with the higher performance during a change in operating conditions. The primary objective is to implement a novel hybrid tracking algorithm to extract the maximum output power from the solar PV panel or array under partial shading conditions (PSCs). This hybrid MPP tracking algorithm is based on the salp swarm algorithm (SSA), which finds the initial global peak (GP) operating point and is followed by the perturb and observation (P&O) algorithm in the last stage to realize a faster convergence rate. Thus, the computational burden met by the conventional methods such as standalone P&O, hybrid grey-wolf-optimization (HGWO), and hybrid whale-optimization algorithm (HWOA) algorithm reported in the literature is overcome by the proposed hybrid SSA algorithm called HSSA. The P&O algorithm searches the MPP in the projected search space by the SSA algorithm. The proposed hybrid algorithm is simulated using MATLAB/Simulink simulation tool to validate the effectiveness of tracking the MPP. The hybrid SSA is compared with the standalone P&O, hybrid WOA, and hybrid GWO, and from the simulation results, it is proved that the hybrid tracking algorithm exhibits a high tracking performance.