New optimization algorithm inspired by fluid mechanics for combined economic and emission dispatch problem

Dong, Ruyi; Wang, Shengsheng

doi:10.3906/elk-1803-88

Cited by 11 publications

(8 citation statements)

References 26 publications

(52 reference statements)

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“…For IPBO achieved comparable emission levels at an improved cost as compared to literature whereas PBO, PBO-CM and PBO-CU showed comparable cost and emissions reviewer. From Table IX, it can be seen than IPBO was able to achieve best cost and best compromise solution as compared to MSA [15], FFA [21], PSOGSA [38], MBFA [35], PSO [17], MOPSO [41], DE [31], MODE/PSO [42], IABC, FSO [33], and NGPSO [40], respectively. In case of IABC [34], IPBO achieved better emission level at comparable cost for best cost solution.…”

Section: Ieee 6-unit Test Systemmentioning

confidence: 99%

“…The outcomes of these problems are beneficial to initiate different demand response actions and demand side flexibility assessment [7][8][9][10]. Several prominent optimizations algorithms that tried to solve these problems include: Genetic algorithm (GA) [11], simulated annealing (SA) [12], differential evolution (DE) [13,14], moth swarm optimization algorithm (MSA) [15], spider monkey optimization (SMO) [16], particle swarm optimization (PSO) [17,18], grey wolf optimizer (GWO) [19], gravitational search algorithm (GSA), fire fly algorithm (FFA) [20,21], harmony search algorithm (HSA) [22,23], spiral optimization algorithm (SOA) [24], squirrel search algorithm (SSA) [25], harris hawks optimization (HHO) [26], sine-cosine algorithm (SCA) [27], artificial bee colony (ABC) [28], bacterial forging algorithm (BFA) [29], flower pollination algorithm (FPA) [30], differential evolution (DE) [31], modified flower pollination algorithm (FPA) [32], , Fluid search optimization (FSO) [33], improved ABC (IABC) [34], modified BFA (MBFA) [35], whale optimization algorithm (WOA) [36], hybrid hierarchical evolution (HHE) [37], hybrid particle swarm gravitational search algorithm (PSOGSA) [38], chaos turbo PSO (CTPSO) [39], new global PSO (NGPSO) [40], multiobjective PSO (MOPSO) [41], multi-objective DE based PSO (MODE/PSO) [42] quantum inspired glowworm swarm optimization (QGSO) [43], combination of cont...…”

Section: Introductionmentioning

confidence: 99%

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Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

et al. 2021

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This paper proposes a novel improved polar bear optimization (IPBO) algorithm and employs it along with polar bear optimization (PBO) and chaotic population-based variants of polar bear optimization algorithm to solve combined economic emission dispatch (CEED) problem. PBO is a meta-heuristic technique inspired by the hunting mechanisms of polar bears in harsh arctic regions based only on their sense of sight. Polar bears in nature exhibits hunting of prey not only on their sight but also on their keen sense of smell. Hence, a novel improved variant of PBO which enhances its operation by equipping it with tracking capabilities utilizing polar bears sense of smell has been proposed in this study. The validity of novel IPBO is tested through 5 benchmark functions and 140 units Korean ED problem. Furthermore, the impact of different population initialization methods is also observed on the capabilities of conventional PBO. The proposed chaotic population based PBO, improved PBO (IPBO) and PBO are employed to solve IEEE 3 unit and 6-unit CEED problem. CEED is a multi-objective power system optimization problem with conflicting objectives of cost and emission. The simulations performed undertake each objective individually as well as collectively. The results achieved by each technique are analyzed statistically through Wilcoxon rank sum test (WRST), probability density function and cumulative density function. Both the statistical and numerical analysis of results showcase the strength of each solution technique as well as their ability to improve cost and emissions in the solution of CEED problem.

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Section: Ieee 6-unit Test Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

et al. 2021

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“…This biobjective CEED optimization problem is converted to a single objective problem using penalty factors and then solved using various potent metaheuristic algorithms and their variants such as improved artificial bee colony algorithm (ABC) in [8], global particle swarm optimization (GPSO) in [9], the chaotic improved harmony search algorithm in [10], flower pollination algorithm in [11], biogeography based optimization in [12], the gravitational search algorithm in (GSA) [13], the stochastic fractal search algorithm in [14], the symbiotic organism search algorithm for multi area power system in [15], fluid mechanism inspired algorithm in [16], and the lightning flash algorithm in [17].…”

Section: A Literature Reviewmentioning

confidence: 99%

“…The power generated by each unit i should lie within limits given by the minimum limit P min i and maximum limit P max i , as shown in (15). The heat output of the i th heat only unit should lie within its limits given by the minimum limit H min i and maximum limit H max i , as shown in (16). The next section describes how to fix the bounds for CHP units.…”

Section: B: Heat Balance Equality Constraintmentioning

confidence: 99%

“…The objective of the CHPEED problem is to obtain the PO solution vector x * that simultaneously minimizes the conflicting objectives given by (6) and 7while satisfying the real power balance equality constraint given by (8), heat balance constraint given by (10), inequality constraints given by (11) to (14) and the bounds given by (15) and (16). The essential goals of the CHPEED problem are 1.…”

Section: ) Feasible Operating Regions (For) Of the Cogeneration Unitsmentioning

confidence: 99%

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Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism

Sundaram

2020

IEEE Access

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This research work proposes a synergistic hybrid metaheuristic algorithm a merger of Nondominated Sorting Genetic Algorithm II and Multiobjective Particle Swarm Optimization algorithm for solving the highly complicated combined heat and power economic emission dispatch problem to operate the power system economically and to reduce the impact of environmental pollution. During the iteration, based on ranking, the population is divided into two halves. The exploration is carried out by Nondominated Sorting Genetic Algorithm II using the upper half of the population. The modification of Multiobjective Particle Swarm Optimization to effectively exploit the lower half of the population is done by increasing the personal learning coefficient, decreasing the global learning coefficient and by using an adaptive mutation operator. To satisfy the linear, nonlinear constraints, and to ensure the populations always lie in the Feasible Operating Region of the cogeneration plant, an effective constraint handling mechanism is developed. The proposed hybrid algorithm with an effective constraint handling mechanism enhances the searching capability by effective information interchange. The algorithm is applied to standard test functions and test systems while considering the valve point effects of the thermal plants, transmission power losses, bounds of the units and feasible operating region of the cogeneration units. The hybrid algorithm can obtain a well spread and diverse Pareto optimal solution and also can converge to the actual Pareto optimal front faster than some of the existing algorithms. The statistical analysis reveals that the proposed hybrid algorithm is a viable alternative to solve this complicated and vital problem.

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An all‐inclusive computer‐aided melanoma diagnosis based on soft computing

Jiao

Zhao

et al. 2022

Int J Imaging Syst Tech

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This paper provides a different optimized approach for melanoma diagnosis from the inputted dermoscopy images. The technique is a pipeline technique with four main steps, including noise reduction, lesion segmentation, feature selection, and final classification. For decreasing the complexity of the feature extraction stage, Fuzzy C-means has been used. The classifier has been improved based on a developed decision tree. The modification of the classifier is based on a new enhanced design of a metaheuristic, called Quantum Fluid Search Optimizer. The efficiency of the suggested technique is calculated by considering some measurement indicators and their achievements are compared with five other latest methods. The results showed the maximum accuracy equal to 94.12% with the highest precision being achieved by the proposed method. The results also indicate that the proposed method with the highest value of 91.18% sensitivity against the other techniques, provides the highest reliability.

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New optimization algorithm inspired by fluid mechanics for combined economic and emission dispatch problem

Cited by 11 publications

References 26 publications

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism

An all‐inclusive computer‐aided melanoma diagnosis based on soft computing

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