As a more efficient power machine at present, diesel engine is widely used in industry, large vehicles, ships, power generation and other industries. Because of its advantages of high thermal efficiency, low fuel cons...
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ISBN:
(纸本)9781450385053
As a more efficient power machine at present, diesel engine is widely used in industry, large vehicles, ships, power generation and other industries. Because of its advantages of high thermal efficiency, low fuel consumption, strong power and long service life that diesel engine will continue to occupy a leading position in its application field in the next few decades.[1] However, because the diesel engine uses complex hydrocarbons as fuel, the air pollution caused by its exhaust gas is very serious, so the development of diesel engine in the future must shift from the concept of only power and economy to paying equal attention to both emission and economy. In this paper, the mainstream algorithm principle is introduced based on Non-dominated Sorting Genetic Algorithms – II. Aiming at solving the optimal solution between these conflicting parameters.
In this work, we explore a novel multi-objectiveoptimization algorithm to identify a set of solutions that could be optimal for more than one task. The proposed approach is used to generate a set of solutions that ba...
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In this work, we explore a novel multi-objectiveoptimization algorithm to identify a set of solutions that could be optimal for more than one task. The proposed approach is used to generate a set of solutions that balance the tradeoffbetween convergence and diversity in multi-objective optimization problems. Equilibrium Optimizer (EO) algorithm is a novel developed meta-heuristic algorithm inspired by the physics laws. In this paper, we propose a multi-objective Equilibrium Optimizer Algorithm (MEOA) for tackling multi-objective optimization problems. We suggest an enhancement for exploration and exploitation factors of the EO algorithm to randomize the values of these factors with decreasing the initial value of the exploration factor with the iteration and increasing the exploitation factor to accelerate the convergence toward the best solution. To achieve good convergence and well-distributed solutions, the proposed algorithm is integrated with the Improvement-Based Reference Points Method (IBRPM). The proposed approach is applied to the CEC 2020, CEC 2009, DTLZ, and ZDT test functions. Also, the inverted generational and spread spacing metrics are used to compare the proposed algorithm with the most recent evolutionary algorithms. It's obvious from the results that the proposed algorithm is better in both convergence and diversity.
This paper develops a multi-objective modeling approach for the scheduling of harvesting resources in the Thai sugar industry, in which different objectives stemming from different industry stakeholders are concurrent...
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This paper develops a multi-objective modeling approach for the scheduling of harvesting resources in the Thai sugar industry, in which different objectives stemming from different industry stakeholders are concurrently optimized with the overall goal to create a more sustainable sugar supply chain. In addition to traditional economic objectives, the environmental impact of sugarcane farm burning is included into the model to better reflect the current harvesting practice, where sugarcane growers often resort to burning their fields due to the lack of available harvesting resources during the season. An evolutionary algorithm based on a variant of Particle Swarm optimization (PSO) is also devised to help solve the resulting multi-objective Harvesting Resource Scheduling problem (MOHRSP), which normally becomes intractable for real-life problem instances. We find that the proposed PSO framework is notably efficient as it provides diverse sets of non-dominated solutions with markedly low coefficients of variation in a reasonable amount of time. We also find that, by sacrificing a slight amount of sugar production volume, the whole sugar supply chain could be largely improved, especially for the sugarcane growers, whose profitability turns out to be sensitive in the trade-offs with other objectives.
In this paper, an improved Competitive Mechanism-based Particle Swarm optimization algorithm called MCMOPSO is presented for multi -objectiveoptimization. The algorithm consists of two main contributions: a new leade...
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ISBN:
(纸本)9781728155586
In this paper, an improved Competitive Mechanism-based Particle Swarm optimization algorithm called MCMOPSO is presented for multi -objectiveoptimization. The algorithm consists of two main contributions: a new leader selection and the analysis of inertia weight. The new multi competition leader selection is introduced which is based on the pairwise competition. It will not only guide the particles to fly to the winner by comparing the nearest angle for two randomly selected elite particles, but also lead the particles to fly to the winner by comparing the nearest angle or farthest angle for several randomly selected elite particles in each iteration. To strike a balance between the exploration and exploitation of the velocity update equation for the original competitive mechanism-based MOPSOalgorithm (CMOPSO), the influence of various inertia weights is investigated to control the previous velocity of each particle. The simulation results show that the proposed algorithm is outperformed four other famous multi -objective particle swarm optimization algorithms in thirty-seven benchmark test problems in terms of inverted generational distance.
multi-stage weapon target assignment (MSWTA) problem is usually treated as a classical constrained multi-objective optimization problem, which tends to balance the attack efficiency and economic loss for weapons after...
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ISBN:
(纸本)9789881563903
multi-stage weapon target assignment (MSWTA) problem is usually treated as a classical constrained multi-objective optimization problem, which tends to balance the attack efficiency and economic loss for weapons after adding engagement resource constraints. In the context of multi-stage confrontations, the MSWTA becomes more complex and challenging to solve. To handle this constrained problem, this paper develops the advantageous two-archive evolutionary algorithm (C-TAEA) to combine the density estimation mechanism with a fast non-dominated sorting method to obtain solutions to this problem efficiently, while maintaining the convergence-oriented archive (CA) to speed up the generation of solutions and the diversity-oriented archive (DA) to enhance the diversity of populations. Extensive experiments are conducted to compare the proposed C-TAEA with the traditional NSGA-II according to the average of IGD and HV values. The numerical simulation results demonstrate that our method intuitively shows the performance improvement after adding the feasibility and can find the approximate Pareto Front more effectively.
In a wide range of contexts including military operations, environment monitoring, surveillance in border areas, health care, public safety [1, 2], disaster management, humanitarian relief and blood supply chain, a ro...
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In a wide range of contexts including military operations, environment monitoring, surveillance in border areas, health care, public safety [1, 2], disaster management, humanitarian relief and blood supply chain, a robust solution of the Covering Salesman problem (CSP) is necessary. These applications require more than one facilities to cover a given customer (region of interest (ROI)). In this paper, we consider the coverage radius to be fixed and same for each node. Then we propose a multi-objective algorithm based on NSGA-II, in which minimization of tour length and maximization of number of nodes with 2-coverage are considered as the objectives. For implementing the algorithm, an individual chromosome is represented using a one-dimensional array with variable length, and developed a new crossover and a new mutation operator based on the problem and variable length chromosome representation. Numerical examples taken from TSP instances (TSPLIB [3]) with number of nodes ranging from 51 to 150 are solved using the algorithm. For each numerical example, the tour corresponding to the solution with 2-coverage of customer nodes is presented, which shows that the proposed algorithm is effective. Finally, a potential future research direction of this problem is discussed. (C) 2021 Elsevier B.V. All rights reserved.
In this paper, a reduced interior-point (RIP) algorithm is introduced to generate a Pareto optimal front for multi-objective constrained optimization (MOCP) problem. A weighted Tchebychev metric approach is used toget...
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In this paper, a reduced interior-point (RIP) algorithm is introduced to generate a Pareto optimal front for multi-objective constrained optimization (MOCP) problem. A weighted Tchebychev metric approach is used together with achievement scalarizing function approach to convert MOCP problem to a single-objective constrained optimization (SOCO) problem. An active-set technique is used together with a Coleman-Li scaling matrix and a decrease interior-point method to solve SOCO problem. A Matlab implementation of RIP algorithm was used to solve three cases and application. The results showed that the RIP algorithm is promising when compared with well-known algorithms and the computations may be superior relevant for comprehending real-world application problems.
Immune systems inspired algorithms with hypermutation operators have achieved great success in solving real-world single-objective as well as multi-objective optimization problems. Compared to the application, however...
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Immune systems inspired algorithms with hypermutation operators have achieved great success in solving real-world single-objective as well as multi-objective optimization problems. Compared to the application, however, the theoretical analysis, particularly on understanding immune-inspired hypermutation operators, is underdeveloped. The few existing theoretical studies mainly focused on single-objectiveoptimization. In this paper, we present a theoretical study for the effectiveness of immune-inspired hypermutation operators in solving multi-objective optimization problems. We compare the expected runtime of a simple multi-objective evolutionary algorithm using four typical immune-inspired hypermutation operators and two classical mutation operators. The results on four bi-objectiveoptimizationproblems widely used in theoretical analysis, namely LOTZ, COCZ, Plateau-MOP and PL, show that using immune-inspired hypermutation operators can always find the Pareto fronts in polynomial expected runtime, which is slower than the best known expected runtime of using classical mutation operators by at most a factor of n. Particularly, on Plateau-MOP and PL, using immune-inspired hypermutation operators can be exponentially faster. This runtime analysis can enhance the understanding of immune-inspired hypermutation operators on solving multi-objective optimization problems, and might be helpful for designing efficient multi-objective evolutionary algorithms in practice.
The algorithms based on decomposition are regarded as a promising optimizer for the multi-objective optimization problems (MOPs). However, it is difficult for the algorithms based on decomposition to handle MOPs with ...
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ISBN:
(纸本)9781450376914
The algorithms based on decomposition are regarded as a promising optimizer for the multi-objective optimization problems (MOPs). However, it is difficult for the algorithms based on decomposition to handle MOPs with the complicated feature, because they adopt the fixed weight vectors. In this paper, we propose an adaptive method with the region detection strategy for the decomposition-based evolutionary algorithm (aMOEA/D-RD) to adjust the weight vectors. In the proposed algorithm, some useless weight vectors, which are not associated to any solution in the successive generations, are found through the region detection method. Then these weight vectors are adaptively adjusted by the solutions in the most crowed subregion. After the adjustment of weight vectors, the distribution of the weight vectors can be more suit to approximate the true the Pareto optimal front of MOPs. Comparative experiments on benchmark with various geometric features have been performed and the simulation results show the effectiveness and the competitiveness of our proposal algorithm.
The modern society has seen a continuously growing electricity consumption and its associated environmental consequences. With recent technology advancements, renewable energy has been considered by many as a source o...
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The modern society has seen a continuously growing electricity consumption and its associated environmental consequences. With recent technology advancements, renewable energy has been considered by many as a source of electricity that is both economically feasible and environmentally friendly. The investment of renewable energy projects can be intriguing, however. This research first developed a theoretical model using multi-objective optimization problem to determine the preferred investment strategies that considers both the economic and environmental benefit of a special kind of investment in renewable energy projects – Corporate Renewable Power Purchase Agreement (PPA). The proposed methods were implemented on the case study of The Pennsylvania State University in central Pennsylvania, United States. The general version of the multi-objective optimization problem required making significant assumptions that reduced the computation complexity. The study explored the uncertainty in future Wholesale Electricity Prices, which was assumed to be the source of electricity for the investors of these renewable energy projects had there been no investments made. The use of Binomial Lattice Pricing Model, Monte Carlo Simulation, and Unit Commitment produced the feasible solutions of the multi-objective optimization problem in which the corresponded Pareto Set was identified. The simplified version of the proposed multi-objective optimization problem was reduced into several Single-objectiveoptimizationproblems of the economic benefits of PPA investments, in which they also represent some Real Option Valuation problems under specific conditions. While making other assumptions to maintain the tractability of these problems, the optimal solutions of the Single-objectiveoptimizationproblem and the Value of Options were identified. One of these Single-objectiveoptimizationproblem monetized the environmental benefits of PPA investments using Social Cost of Carbon publishe
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