Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.

Multiobjective evolutionary algorithms: A survey of the state of the art

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.

Review: A state-of the-art survey of TOPSIS applications

The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur's entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur's entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.

Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy

Some uncertainties, such as the uncertain output power of a plug-in electric vehicle (PEV) due to its stochastic charging and discharging schedule, that of a wind generation unit due to the stochastic wind speed, and that of a solar generating source due to the stochastic illumination intensity, volatile fuel prices, and future uncertain load growth could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distribution system planning. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal siting and sizing of DGs. First, a mathematical model of CCP is developed with the minimization of the DGs' investment cost, operating cost, maintenance cost, network loss cost, as well as the capacity adequacy cost as the objective, security limitations as constraints, and the siting and sizing of DGs as optimization variables. Then, a Monte Carlo simulation-embedded genetic-algorithm-based approach is employed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is used to verify the feasibility and effectiveness of the developed model and method, and the test results have demonstrated that the voltage profile and power-supply reliability for customers can be significantly improved and the network loss substantially reduced.

Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties

Traditionally, mineral oil is used as insulating fluids in transformers. The environmental impact of these mineral oils is often characterized in past centuries. In order to overcome the above facts, Vegetable oil is used as alternative source for mineral oil. Vegetable oil has higher biodegradability and it is treated as more environmental friendly fluids. In this work, the refined natural esters like Sunflower oil, Rice bran oil and Corn oil are chosen and mixed in various proportions. The critical parameters like Breakdown Voltage, Flash Point, Fire Point and Viscosity are determined for these mixed insulating fluids as per ASTM standards. These results in higher Breakdown voltage values compared to other samples.

Investigation of critical parameters of mixed insulating fluids

Power transformers are one of the critical components in power system. Insulation plays a promistic role in determining the lifetime of transformers. Insulation used in transformers is of solid and liquid insulation. Solid insulation like kraft paper, press board on degradation process generates furanic derivatives such as 5-methyl furan, 2-acetyl furan, 2-hydroxymethyl furan and 2-furaldehyde. Traditionally, the presence of furanic compounds in transformer oil is determined using high-sperformance liquid chromatography. An effort has been made to estimate the presence of furanic derivatives using artificial neural network (ANN) with the aid of ultra-violet–visible light absorbance property. In ANN system, Levenberg Marquardt back propagation optimisation technique is used to determine the furanic derivatives in transformer oil. Degree of polymerisation of insulation paper and percentage tensile strength of paper remaining has been examined.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-epa.2012.0177

Investigations of furanic derivatives in transformer oil using ultra-violet light absorption property

The paper addresses the serious issue on insulators owing to partial discharge and flashover resulted from severe NaCl salt contaminant accumulation on transmission lines insulators located near the coastal areas. Disc type insulators such as porcelain and glass were tested at various NaCl salt pollution level in an artificial fog chamber according to IEC60507 standard. The leakage currents associated with insulator types were recorded continuously at equal operating conditions (voltage, temperature and pressure). The statistical data of the leakage current such as the mean value (I mean ), maximum value (I max ) and standard deviation (σ) are considered. The recorded leakage currents are employed in Neural Network model as an input and using Feed Forward Back Propagation Network, the resultant contamination severity is predicted. This model is suitable to predict the Equivalent Salt Deposit Density (ESDD). The Neural Network Model is used to determine (predict) the intensity of leakage current (in mA) during security stage (<50mA) in the course of inception voltage.

Evaluation and prediction of contamination level in coastal region insulators based on leakage current characteristics

The buck converter is one of the DC/DC converters that are often used as power supplies and high energy conversion. These controllers are used in electronic devices to regulate the output voltage a...

Design and Experimental Validation of PID Controller for Buck Converter: A Multi-Objective Evolutionary Algorithms Based Approach

In this paper, Multiobjective Mobile Antenna Location identification problem is considered with site coordinates (x, y), transmitting power, height, and tilt angle as design parameters and maximization of service coverage and minimization of cost as objectives. Evolutionary multiobjective algorithm NSGA-II and modified NSGA-II by incorporating dynamic crowding distance and controlled elitism is considered. A 15×15 Km2 synthetic test system is taken for simulation purposes. From the simulation results, we realize that modified NSGA-II is able to generate better Paretofront in terms of diversity and uniformity.

M. Willjuice Iruthayarajan

Papers

Investigation of critical parameters of mixed insulating fluids

Investigations of furanic derivatives in transformer oil using ultra-violet light absorption property

Evaluation and prediction of contamination level in coastal region insulators based on leakage current characteristics

Design and Experimental Validation of PID Controller for Buck Converter: A Multi-Objective Evolutionary Algorithms Based Approach

Multiobjective Mobile Antenna Location identification using evolutionary optimization algorithm