Multi-Objective based Generation Fuel Cost and Emission Reduction using Grasshopper Optimization Algorithm

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization (PSO) algorithm was constructed. As a new surrogate model technology, Kriging model has better fitting precision for nonlinear problem. The Kriging model was adopted to replace computer aided engineering (CAE) simulation as fitness function of multi-objective PSO algorithm, and the computation cost can be reduced greatly. By introducing multi-objective handling mechanism of crowding distance and mutation operator to multi-objective PSO algorithm, the entire Pareto front can be approximated better. It is shown that the multi-objective optimization strategy can get higher solving accuracy and computation efficiency under small sample.

In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis.

Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture's optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others.

Economic Environmental Dispatch (EED) problem is the most serious problem in power operations and management. Taking into consideration transmission losses, power balance constraint, and generator capacity constraint, the EED problem becomes a non convex optimization problem, which is a difficult task that cannot be solved with traditional techniques. Furthermore, with both the environmental issues and economic objectives, the EDD problem can be formulated as a constrained nonlinear multiobjective optimization problem. In the present work, an enhanced multiobjective particle swarm optimization algorithm (EMOPSO) is proposed and applied to solve the EED problem. The proposed approach EMOPSO extends the particle swarm optimization to deal with multiobjective continuous optimization problems, EMOPSO incorporates a crowding distance mechanism for leader selection and deletion in an external archive of non dominated solutions, this mechanism improves the diversity and uniformity of the pareto front. A mutation operator is used in the population to prevent the premature convergence of the algorithm. The simulations have been made on IEEE 30 bus test system with six generators. The results are satisfying, and show that the proposed EMOPSO method was capable of achieving better performance compared with other multiobjective algorithms in the literature.

The current paper presents a novel multi-objective particle swarm optimization (MOPSO) algorithm for solving no-wait flow shop scheduling problems with makespan and maximum tardiness criteria. First, in the algorithm, particles are represented as job permutations and updated directly in the discrete domain. Second, the concept of Pareto dominance is applied to compare different solutions of multi-objective optimization, and a set is employed to hold and to update the obtained non-dominated solutions, where a randomly selected non-dominated solution is assigned as the global best particle to maintain the diversity of the searching direction and to speed up the convergence process to the Pareto front. Third, a new multi-objective heuristic, named the PWQ (Pan—Wang—Qian) heuristic, is proposed to produce a population of initial particles with relatively good performances. Fourth, a simple but effective multi-objective local search algorithm is developed to embed in the MOPSO algorithm for stressing the balance between global exploration and local exploitation. In addition, two speed-up methods are devised to evaluate a job permutation and its insert neighbourhood respectively. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed algorithm is superior to a recently published multi-objective hybrid differential evolution (MHDE) algorithm in terms of searching quality, diversity level, robustness, and efficiency.

In this paper, a multi-objective particle swarm optimization algorithm based on the relative distance strategy is proposed to realize hydrodynamic coefficient identification, which can improve the accuracy of parameter identification without determining the weight manually. Firstly, single-objective particle swarm optimization (PSO) is used to identify the parameters of the model with a low coupling degree. Then, the parameters of the strongly coupled sway and yaw motion model are regarded as multi-objective, and a set of unbiased Pareto optimal solutions are obtained by the multi-objective particle swarm optimization (MOPSO) algorithm. Then the distance function of the Pareto solution is constructed to determine the parametric solution of the model. Finally, experimental identification is carried out by using the data set collected from the real ship. Experimental results show that the accuracy of model parameters obtained by this identification method is about 91.6%, and there is no need to set the weighting coefficient.

In order to enhance the convergence and distribution of multi-objective particle swarm algorithm, an improved multi-objective particle swarm optimization algorithm was proposed. Linear decreasing inertia weight was used to update. The method can improve the deficiency that the algorithm falls into local optimal easily. The improved Logistic mapping was used to increase ergodicity of the particles. The method can expand the search scope. At the same time, the elite archiving mechanism and the mutation probability were introduced to increase the disturbance. The method can improve the local optimal. Compared with the real Pareto front, NSGA-II and MOEAD algorithm, the simulation shows that the algorithm proposed in the paper is effective.

In the railway station scheduling system under the water-rail combined transport mode, how to reasonably allocate equipment resources according to the operation tasks, improve the efficiency of mechanical loading and unloading, and reduce the cost of loading and unloading operations, has become an urgent problem in the process of building a modern port. In this paper, a railway station scheduling algorithm based on multi-objective particle swarm optimization algorithm (RSSAMOPSO) was designed. The multi-objective particle swarm algorithm was used to solve the multi-objective optimization problem of railway station scheduling. Aiming at the problems of decreasing con-vergence speed and the lost of the diversity of the solutions, a compression factor and an adaptive mutation operator are introduced. The improved multi-objective particle swarm algorithm (IM_MOPSO) is combined with the compression factor and adaptive mutation operator to obtain the optimal operation sequence and various equipment task allocation schemes. Experimental results show that the above method has a large practical significance and practical value for improving the efficiency of mechanical loading and unloading in the railway yard and reducing the operating cost of the railway yard.

Background: The extensive utilization of the dual active bridge (DAB) topology in the domain of power electronics has led to the prevalence of DC/AC converters based on DAB that adopt two-stage conversion and phase-shifting control strategies in their design. This has resulted in an increase in losses and complexity in practice. Objective: The objective of this study is to propose a parameter optimization method for a singlestage isolated bidirectional DC/AC converter, utilizing a hybrid control method of variable frequency and phase shift control to enhance the efficiency of the converter. Method: The use of a single-stage topology eliminates the DC link composed of decoupling capacitors in the middle. Subsequently, the equal power method was employed to facilitate frequency conversion and phase shift control. Furthermore, the LC parameters of the converter were optimized based on the multi-objective ant lion optimizer. Results: A simulation model was constructed based on a single-stage grid-connected system. In comparison to the multi-objective particle swarm optimization algorithm and the multi-objective sparrow algorithm, the LC parameters optimized by the multi-objective ant lion algorithm demonstrated a reduction in the THD of grid-connected current by 27% and 66%, respectively. The optimized parameters result in a reduction in the harmonic distortion rate of the grid-connected current to 1.3% and an improvement in efficiency to 90%. Conclusion: The simulation results have demonstrated the efficacy and precision of the parameter optimization strategy, and the proposed optimization strategy has the potential to enhance the performance of grid-connected systems.

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

The initial Moth-Flame Optimization (MFO) algorithm is appropriately changed to deal with multi-objective optimization problems described as Multi-objective Moth-Flame Optimization (MMFO). The primary idea of this MFO is the navigating technique of moths in nature which fly in night time by keeping a fixed angle relative to the moon. Nevertheless, these elegant bugs are entrapped in a useless/deadly spiral route around fabricated lights. Normally principles like using fixed-sized external archive make the recommended technique vary from the initial single-objective MFO. The efficiency of recommended MMFO is tested on eight multi-objective engineering design problems concerning remarkable precision and uniformity compared to Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Gray Wolf Optimizer (MOGWO), and Multi-objective Ant Lion Optimizer (MOALO). According to the results of different performance metrics, such as Generational Distance (GD), Inverted Generational Distance (IGD), and Maximum Spread (MS), the proposed algorithm can provide quality Pareto fronts with very competitive results.

A multi-objective particle swarm optimization algorithm based on two-archive mechanism

A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

Distributed energy resource allocation using multi-objective grasshopper optimization algorithm