Multi-objective optimization of a mixture inventory system using a MOPSO–TOPSIS hybrid approach

Hydrological models are necessary tools for simulating the water cycle and for understanding changes in water resources. To achieve realistic model simulation results, real-world observations are used to determine model parameters within a “calibration” procedure. Optimization techniques are usually applied in the model calibration step, which assures a maximum similarity between model outputs and observations. Practical experiences of hydrological model calibration have shown that single-objective approaches might not be adequate to tune different aspects of model simulations. These limitations can be as a result of (i) using observations that do not sufficiently represent the dynamics of the water cycle, and/or (ii) due to restricted efficiency of the applied calibration techniques. To address (i), we assess how adding daily Total Water Storage (dTWS) changes derived from the Gravity Recovery And Climate Experiment (GRACE) as an extra observations, besides the traditionally used runoff data, improves calibration of a simple 4-parameter conceptual hydrological model (GR4J, in French: modèle du Génie Rural à 4 paramètres Journalier) within the Danube River Basin. As selecting a proper calibration approach (in ii) is a challenging task and might have significant influence on the quality of model simulations, for the first time, four evolutionary optimization techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-objective Particle Swarm Optimization (MPSO), the Pareto Envelope-Based Selection Algorithm II (PESA-II), and the Strength Pareto Evolutionary Algorithm II (SPEA-II) along with the Combined objective function and Genetic Algorithm (CGA) are tested to calibrate the model in (i). A number of quality measures are applied to assess cardinality, accuracy, and diversity of solutions, which include the Number of Pareto Solutions (NPS), Generation Distance (GD), Spacing (SP), and Maximum Spread (MS). Our results indicate that according to MS and SP, NSGA-II performs better than other techniques for calibrating GR4J using GRACE dTWS and in situ runoff data. Considering GD as a measure of efficiency, MPSO is found to be the best technique. CGA is found to be an efficient method, while considering the statistics of the GR4J’s 4 calibrated parameters to rank the optimization techniques. The Nash-Sutcliffe model efficiency coefficient is also used to assess the predictive power of the calibrated hydrological models, for which our results indicate satisfactory performance of the assessed calibration experiments.

Heuristic-based multi-objective optimization design for viscous dampers in RC frame structures considering soil-structure interaction

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

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.

Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory system

A method for designing miniaturized frequency selective surface (FSS) using equivalent circuit model (ECM) and multi-objective particle swarm optimization (MOPSO) algorithm is proposed in this paper. In the proposed method, ECM describing the desired frequency response is established, and parameters of the ECM are determined using the MOPSO algorithm. Combining the ECM analysis and the MOPSO algorithm, the requirements of desired frequency response can be converted into the constraints of equivalent circuit parameters, which simplifies the optimization process of FSS design. For verification, a single-layer bandstop-bandpass FSS composed of convoluted elements with the unit cell size of 0.073λ 0 × 0.073λ 0 is designed. Simulation results show that the −15 dB bandwidth is from 2.62 to 2.83 GHz, and the −0.5 dB bandwidth is from 3.84 to 5 GHz. Meanwhile, the resonant frequency is stable at different incident angles (up to 80°) under TE and TM polarization. Finally, the proposed FSS prototype is fabricated and measured. The measured results agree well with the simulated ones.

A multi-objective differential evolution-chaos shuffled frog leaping algorithm (MODE-CSFLA) is proposed for water resources system optimization to overcome the shortcomings of easily falling into local minima and premature convergence in SFLA. The performance of MODE-CSFLA in solving benchmark problems is compared with that of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO). At last, the proposed MODE-CSFLA is used to optimize the water resources allocation plan of the East Route of the South-to-North Water Transfer Project in the normal, dry, and extremely dry years. The results reveal that MODE-CSFLA performs better than NSGA-II and MOPSO under all conditions. Compared with shuffled frog leaping algorithm (SFLA), MODE-CSFLA can result in a 29.39, 27.47 and 22.55% increase in water supply when the single objective is to minimize the water pumpage; and a 41.01, 39.63 and 30.94% decrease in total pumpage when the single objective is to maximize the water supply in the normal, dry, and extremely dry conditions, respectively. Thus, MODE-CSFLA has the potential to be used for solving complex optimization problems of water resources systems.

Non-orthogonal multiple access (NOMA) deployment in future wireless networks has been recently considered a promising radio access technology to enhance spectral efficiency (SE). However, gain in SE comes always with the cost of energy efficiency (EE). In this paper, we investigate the SE and EE tradeoff in downlink NOMA with the consideration of quality of service (QoS) requirements based on three population-based multi-objective evolutionary algorithms (MOEAs): multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm-II (NSGA-II) and strength Pareto evolutionary algorithm-2 (SPEA2). The tradeoff is optimized and Pareto optimal solutions are obtained through MOEAs. The effectiveness of the algorithms is evaluated based on the hypervolume metric and the capability of solving multi-objective optimization problems. Simulation results reveal that SPEA2 outperforms NSGA-II and MOPSO. Furthermore, NSGA-II is the loser among all algorithms in terms of finding Pareto optimal results.

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.

This paper addresses a multi-objective approach for simultaneous dynamic expansion planning of conventional sub-transmission grid and Regional Virtual Power Plant (RVPP). In such a feed-in tariff electricity market, it will assist RVVP’s stakeholders in deciding whether or not they should invest in new equipment’s installation. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm as a heuristic optimization method is proposed to eliminate the conventional centralized planning, which has led to competition between Regional Electric Company (REC) and RVPP for power delivery in sub-transmission system. Two objective functions are considered for simultaneous expansion planning of these two systems. The first one takes the minimum cost of the REC, as the sub-transmission grid operator, into account while the other one considers profit maximization for RVPP. To achieve the goals, MOPSO algorithm is employed to find the best expansion of REC and best location and capacity of RVPP’s resources. Having solved the proposed multi-objective optimization problem, a Pareto front is determined to show the trade-off between REC and RVPPs' contributions in joint optimal expansion planning of conventional sub-transmission grid and the RVPP. To demonstrate the applicability and effectiveness of the proposed approach, a realistic sub-transmission system in Guilan Province, Iran is used as a test system, and the results are evaluated accordingly.

The transition from fossil fuels to renewable energy demands energy systems that are not only technically and economically sound but also environmentally and socially sustainable. This study proposed a novel multi-objective optimisation (MOO) framework for the design of a grid-connected hybrid renewable energy system (GC-HRES) that explicitly integrated Job Creation (JC) as a fourth objective along with traditional technical, economic, and environmental objectives, such as Loss of Power Supply Probability (LPSP), Cost of Energy (COE), and Renewable Energy Fraction (REF). Prior studies often treated JC as a post-optimisation metric, while this study incorporated JC into the MOO using employment technology-specific factors. Metaheuristic algorithms, Multi-Objective Particle Swarm Optimisation (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), were applied to find the optimal number of solar panels, wind turbines, and battery banks under scenarios which excluded and included JC. The results demonstrated that including JC reshaped the Pareto front, revealed new objective compromises and led to diverse configurations. MOPSO favoured solutions with higher JC and REF at the expense of cost and reliability, while NSGA-II achieved more balanced, cost-effective and reliable solutions with competitive JC values. Additional constraint sensitivity analysis further demonstrated the influence of battery bank constraints on solution feasibility. The results highlighted that integrating JC into the optimisation framework enriched design possibilities and fostered a more inclusive transition to renewable energy with a focus on GC-HRESs. This paper provided a flexible and replicable MOO framework that sets the foundation for socially attuned GC-HRES designs that align with broader sustainability goals.

The process planning decisions on detailed steps of machining a part including selection of operations, machines, tools and operation sequences are crucial, directly impacting the carbon emissions associated with machining. Operation sequencing generates the optimal order of operations by reducing production time and cost. A need is felt to explore operation sequence optimization by balancing economic and environmental considerations that very few studies have so far addressed. Our study proposes an approach for multiobjective operation sequence optimization by simultaneously minimizing carbon emission and total production cost in machining. Firstly, machining operations are selected and carbon emissions are quantified for individual operations. Secondly, the proposed multiobjective optimization model for operation sequencing is applied. Due to NP-hard nature of this problem due to the exponential growth of potential solution sequences as the number of operations increases, making it computationally challenging to find an optimal sequence, it is often challenging and very time-consuming to solve using exact mathematical models. However, metaheuristic approaches can potentially offer near-optimal solutions in a more reasonable time frame. We implemented Non-dominant Sorting Genetic Algorithm-II(NSGA-II), Multiobjective Particle Swarm Optimization (MOPSO) and Multiobjective Ant colony Optimization (MOACO). Finally, a case study is used to validate and compare their effectiveness in providing low-carbon emissions with cost-effective sequences. NSGA-II is found to have higher computational time but a superior Pareto tradeoff between reducing carbon emissions and production costs.

Tolerance design has become a very key issue in product and process development because of an informal compromise between functionality, quality, and manufacturing cost. The problem formulation becomes complex with simultaneous selection of design and manufacturing tolerances and the optimization problem is difficult to solve with the traditional optimization techniques. In this paper, a recently developed optimization algorithm called teaching-learning-based optimization (TLBO) is used for optimal selection of design and manufacturing tolerances with an alternative manufacturing process to obtain the optimal solution which is nearer to global optimal solution. Three problems are considered and these are: overrunning clutch assembly, knuckle joint assembly with three arms, and a helical spring. Out of these three problems, the problems of overrunning clutch assembly and knuckle joint assembly with three arms are multi-objective optimization problems and the helical spring problem is a single-objective problem. The comparison of the proposed algorithm is made with the genetic algorithm (GA), Non-dominated sorting genetic algorithm-II (NSGA-II), and multi-objective particle swarm optimization algorithm (MOPSO). It is found that the TLBO algorithm has produced better results when compared to those obtained by using GA, NSGA-II, and MOPSO algorithms.

This communication addresses the thinned linear array design for the optimization of radiometric sensitivity and angular resolution, both of which are crucial characteristics of interferometric aperture synthesis radiometers (IASRs). First, we establish a multiobjective optimization model concerning their tradeoff. Then, three state-of-the-art search algorithms, namely, multiobjective particle swarm optimization (MOPSO), nondominated sorting genetic algorithm II (NSGA-II), and nondominated neighbor immune algorithm (NNIA), are implemented for the design of linear interferometric arrays. Furthermore, a constraint principle of interferometric arrays is proposed to reveal the inherent relation among array parameters closely related to IASR performances, which would be useful for the multiobjective optimization of IASR arrays.