A multi-objective differential evolutionary algorithm for constrained multi-objective optimization problems with low feasible ratio

Yang, Yongkuan; Liu, Jianchang; Shi, Tan; Wang, Honghai

doi:10.1016/j.asoc.2019.02.041

Cited by 84 publications

(17 citation statements)

References 25 publications

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“…With the increasing environmental protection awareness, the study on CHPEED has been carried out extensively. At present, three kinds of multi-objective evolutionary algorithm paradigms are widely used [17]: Pareto-based [18] approaches, indicator-based approaches and decompositionbased [19] approaches. The most classical Pareto-based algorithm is NSGA-II [20].…”

Section: Literature Surveymentioning

confidence: 99%

Indicator & crowding distance-based evolutionary algorithm for combined heat and power economic emission dispatch

Sun

Deng

2020

Applied Soft Computing

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Heat and power have become the most indispensable resources. However, the traditional ways of generating power and heat are inefficient and cause high pollution; a CHP (Combined Heat and Power) unit can solve these problems well. In recent years, more attention has been paid to energy conservation and environmental protection, and Combined Heat and Power Economic Emission Dispatch (CHPEED) has become an important multi-objective optimization problem. In this paper, an Indicator & crowding Distance-based Evolutionary Algorithm (IDBEA) is put forward for handling this non-convex and non-linear problem. With consideration of the valve-point effects and power transmission loss, IDBEA is tested on three standard test systems with different types, including four units, five units and seven units. In the experiment, IDBEA is compared with several evolutionary algorithms, the simulation results demonstrate that IDBEA has strong stability and superiority, while the solutions show better convergence and diversity than several typical algorithms.

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Section: Literature Surveymentioning

confidence: 99%

Indicator & crowding distance-based evolutionary algorithm for combined heat and power economic emission dispatch

Sun

Deng

2020

Applied Soft Computing

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“…In the mathematical multiobjective optimization formalism, a generic constrained problem is defined as follows [26]:…”

Section: B Problem Formulationmentioning

confidence: 99%

Multi-Verse Algorithm based Approach for Multi-criteria Path Planning of Unmanned Aerial Vehicles

Jarray¹

2020

IJACSA

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In this paper, a method based on a Multiobjective Multi-Verse Optimizer (MOMVO) is proposed and successfully implemented to solve the unmanned aerial vehicles' path planning problem. The generation of each coordinate of the aircraft is reformulated as a multiobjective optimization problem under operational constraints. The shortest and smoothest path by avoiding all obstacles and threats is the solution of such a hard optimization problem. A set of competitive metaheuristics such as Multiobjective Salp Swarm Algorithm (MSSA), Grey Wolf Optimizer (MOGWO), Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are retained as comparison tools for the problem's resolution. To assess the performance of the reported algorithms and conclude about their effectiveness, an empirical study is firstly performed for solving different multiobjective test functions from the literature. These algorithms are then used to obtain a set of optimal Pareto solutions for the multi-criteria path planning problem. An efficient Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) of Multi Criteria Decision-Making (MCDM) model is investigated to find the optimal solution from the non-dominant ones. Demonstrative results and statistical analysis are presented and compared in order to show the effectiveness of the proposed MOMVO-based path planning technique.

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“…From the perspective of DE algorithms, the focus of the current research is to avail the effectiveness of DE in deriving better solutions by reducing the search space while the constrained opinions are solicited from crowd. Although various DE algorithms are proposed [32], [47], [48], however, in this current context, the objective is to utilize DE algorithm from multi-objective perspective to tackle the crowd judgment analysis problem. Due to the various challenges present in crowdsourced opinions, therefore, the various steps of the proposed DE are needed to be designed carefully considering the factors specific to problem.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Objective Differential Evolutionary Method for Constrained Crowd Judgment Analysis

Chatterjee

Lim

2020

IEEE Access

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Crowdsourcing has already been shown to be a promising tool in solving many real-life problems in time and cost-effective way. For example, in city planning, to install some specific facilities it is required to acquire knowledge about various factors like demand, demographic information, suitability of the resources in that area, etc. However, obtaining this information is a tedious and time-consuming job. Now-a-days, this process can be accelerated by utilizing the enormous power of crowd while outsourcing it to the general people. Basically, seeking opinions from multiple non-experts instead of a single expert can be advantageous in terms of time, cost and accuracy. Although, in most of the crowdsourcing models, the questions posted to crowd consist of a single component. Interestingly, in many real-life applications like city planning, the questions can have multiple components. To exemplify, the posted question can be seeking opinions about 2D coordinates of k best possible locations (i.e., k components) to install k facilities. Moreover, there exist some constraints which are needed to be satisfied by the crowd while providing their opinions. Thus, it introduces a new kind of judgment analysis problem recently termed as 'Constrained Judgment Analysis'. Most of the state-of-the-art judgment analysis problems deal with the question without multiple components and constraints as well. In this article, we address this emerging problem and propose a multi-objective differential evolution method to obtain better decision guided by the crowd. The effectiveness of the proposed method is demonstrated by applying it over two real-life crowd opinion datasets. INDEX TERMS Crowdsourcing, judgment analysis, multi-objective optimization.

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A multi-objective differential evolutionary algorithm for constrained multi-objective optimization problems with low feasible ratio

Cited by 84 publications

References 25 publications

Indicator & crowding distance-based evolutionary algorithm for combined heat and power economic emission dispatch

Indicator & crowding distance-based evolutionary algorithm for combined heat and power economic emission dispatch

Multi-Verse Algorithm based Approach for Multi-criteria Path Planning of Unmanned Aerial Vehicles

A Multi-Objective Differential Evolutionary Method for Constrained Crowd Judgment Analysis

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