<tex> $I_{\rm SDE}$</tex>+—An Indicator for Multi and Many-Objective Optimization

Pamulapati, Trinadh,Mallipeddi, Rammohan,Suganthan, Ponnuthurai Nagaratnam Institute of Electrical and Electronics Engineers 2019 IEEE transactions on evolutionary computation Vol.23 No.2

<P>In this letter, an efficient indicator for multi and many-objective optimization is proposed. The proposed indicator ( <TEX>$I_{{SDE}}$</TEX><SUP>+</SUP>) is a combination of sum of objectives and shift-based density estimation and benefits from their ability to promote convergence and diversity, respectively. An evolutionary multiobjective optimization framework based on the proposed indicator is shown to perform comparably or better than the state-of-the-art on a variety of scalable benchmark problems.</P>

Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques

Biswas, Partha P.,Suganthan, P.N.,Mallipeddi, R.,Amaratunga, Gehan A.J. Elsevier 2018 ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENC Vol.68 No.-

<P><B>Abstract</B></P> <P>Optimal power flow (OPF) is a highly non-linear complex optimization problem where the steady state parameters of an electrical network need to be determined for its economical and efficient operation. The complexity of the problem escalates with ubiquitous presence of constraints in the problem. Solving OPF remains a popular but challenging task among power system researchers. In last couple of decades, numerous evolutionary algorithms (EAs) have been applied to find optimal solutions with different objectives of OPF. However, the search method adopted by EAs is unconstrained. An extensively used methodology to discard infeasible solutions found during the search process is the static penalty function approach. The process requires appropriate selection of penalty coefficients decided largely by tedious trial and error method. This paper presents performance evaluation of proper constraint handling (CH) techniques — superiority of feasibly solutions (SF), self-adaptive penalty (SP) and an ensemble of these two constraint handling techniques (ECHT) with differential evolution (DE) being the basic search algorithm, on the problem of OPF. The methods are tested on standard IEEE 30, IEEE 57 and IEEE 118-bus systems for several OPF objectives such as cost, emission, power loss, voltage stability etc. Single objective and weighted sum multi-objective cases of OPF are studied under the scope of this literature. Simulation results are analyzed and compared with most recent studies on the problem.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Optimal power flow problems for IEEE 30, 57 and 118-bus systems are studied. </LI> <LI> Several single and multi-objective case studies are performed. </LI> <LI> Performances of 3-constraint handling techniques are analyzed. </LI> <LI> Differential evolution (DE) is used as basic search algorithm. </LI> <LI> Results are compared with most recent studies and improvement is observed. </LI> </UL> </P>

Differential evolution with multi-population based ensemble of mutation strategies

Wu, G.,Mallipeddi, R.,Suganthan, P.N.,Wang, R.,Chen, H. North-Holland [etc ; Elsevier Science Ltd 2016 Information sciences Vol.329 No.-

<P>Differential evolution (DE) is among the most efficient evolutionary algorithms (EAs) for global optimization and now widely applied to solve diverse real-world applications. As the most appropriate configuration of DE to efficiently solve different optimization problems can be significantly different, an appropriate combination of multiple strategies into one DE variant attracts increasing attention recently. In this study, we propose a multi-population based approach to realize an ensemble of multiple strategies, thereby resulting in a new DE variant named multi-population ensemble DE (MPEDE) which simultaneously consists of three mutation strategies, i.e., 'current-to-pbest/1' and 'current-to-rand/1' and 'rand/1'. There are three equally sized smaller indicator subpopulations and one much larger reward subpopulation. Each constituent mutation strategy has one indicator subpopulation. After every certain number of generations, the current best performing mutation strategy will be determined according to the ratios between fitness improvements and consumed function evaluations. Then the reward subpopulation will be allocated to the determined best performing mutation strategy dynamically. As a result, better mutation strategies obtain more computational resources in an adaptive manner during the evolution. The control parameters of each mutation strategy are adapted independently as well. Extensive experiments on the suit of CEC 2005 benchmark functions and comprehensive comparisons with several other efficient DE variants show the competitive performance of the proposed MPEDE (Matlab codes of MPEDE are available from http://guohuawunudt.gotoip2.com/publications.html). (C) 2015 Elsevier Inc. All rights reserved.</P>

A variable reduction strategy for evolutionary algorithms handling equality constraints

Wu, G.,Pedrycz, W.,Suganthan, P.N.,Mallipeddi, R. Elsevier Science, B.V 2015 Applied soft computing Vol.37 No.-

Efficient constraint handling techniques are of great significance when Evolutionary Algorithms (EAs) are applied to constrained optimization problems (COPs). Generally, when use EAs to deal with COPs, equality constraints are much harder to satisfy, compared with inequality constraints. In this study, we propose a strategy named equality constraint and variable reduction strategy (ECVRS) to reduce equality constraints as well as variables of COPs. Since equality constraints are always expressed by equations, ECVRS makes use of the variable relationships implied in such equality constraint equations. The essence of ECVRS is it makes some variables of a COP considered be represented and calculated by some other variables, thereby shrinking the search space and leading to efficiency improvement for EAs. Meanwhile, ECVRS eliminates the involved equality constraints that providing variable relationships, thus improves the feasibility of obtained solutions. ECVRS is tested on many benchmark problems. Computational results and comparative studies verify the effectiveness of the proposed ECVRS.

A multiobjective approach for optimal placement and sizing of distributed generators and capacitors in distribution network

Biswas, Partha P.,Mallipeddi, R.,Suganthan, P.N.,Amaratunga, Gehan A.J. Elsevier 2017 Applied soft computing Vol.60 No.-

<P>Both active and reactive power play important roles in power system transmission and distribution networks. While active power does the useful work, reactive power supports the voltage that necessitates control from system reliability aspect as deviation of voltage from nominal range may lead to inadvertent operation and premature failure of system components. Reactive power flow must also be controlled in the system to maximize the amount of real power that can be transferred across the power transmitting media. This paper proposes an approach to simultaneously minimize the real power loss and the net reactive power flow in the system when reinforced with distributed generators (DGs) and shunt capacitors (SCs). With the suggested method, the system performance, reliability and loading capacitycan be increased by reduction of losses. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) is adopted to select optimal sizes and locations of DGs and SCs in large scale distribution networks with objectives being minimizing system real and reactive power losses. MOEA/D is the process of decomposition of a multiobjective optimization problem into a number of scalar optimization subproblems and optimizing those concurrently. Case studies with standard IEEE 33-bus, 69-bus, 119-bus distribution networks and a practical 83-bus distribution network are performed. Output results of MOEA/D method are compared with similar past studies and notable improvement is observed. (C) 2017 Elsevier B.V. All rights reserved.</P>

Haploid embryogenesis and molecular detection of somatic embryogenesis receptor-like kinase (TcSERK) genes in sliced ovary cultures of cocoa (Theobroma cacao L.)

Ramasamy Gnanam,Ramasamy Sivachandran,Ravi Nagganatha Suganthan,Krishnan Rajalakshmi,Subramanian Rajesh,Raman Renuka,Duraialaguraja Sudhakar,Muthurajan Raveendran,Vellaichamy Jegadeeswari 한국식물생명공학회 2022 Plant biotechnology reports Vol.16 No.3

Somatic embryos were induced from sliced unpollinated ovaries of cocoa. The influence of genotypes and cold pre-treatment were studied on the induction of callus and haploid embryogenesis. Among the five cocoa genotypes studied, CCRP 5 and CCRP 1 had recorded a maximum callus induction frequency of 66.00% and 62.00%, respectively, from sliced ovaries on WPM medium supplemented with 2-iP (1.0 mg L−1), Zeatin (1.0 mg L−1), and AgNO3 (5.0 mg L−1). Sliced ovaries isolated from cold pre-treated (4 °C for 1 day), 4–6 mm long (containing mature ovule) flower buds recorded the maximum callus induction frequency (67%). The highest percentage of embryogenic calli was noticed from ovaries of pre-treated (4 °C for 1 day) flower buds of CCRP 5 (31.00%). During proliferation and sub-culturing, callus morphogenesis such as white com- pact, light creamy nodular, proliferative beige shaded embryogenic, and light brown watery spongy non-proliferative calli were observed. Induction of globular and heart stage somatic embryos was noticed in WPM medium supplemented with ascorbic acid (35.2 mg L−1), Zeatin (1.0 mg L−1), Kinetin (3.0 mg L−1), and sucrose (30.0 g L−1). Further, the cotyledon- ary stage embryos and shoot conversion were observed in WPM medium supplemented with MgSO4 (4.0 g L−1), K2SO4 (12.0 g L−1), glucose (1.0 g L−1), and sucrose (30.0 g L−1). Histological and scanning electron microscopic studies revealed an asynchronous pattern of somatic embryos development (globular, heart, and torpedo stage) from embryogenic calli. The molecular confirmation of embryogenic competence with different types of ovary callus at different stages was confirmed with the detection of the TcSERK gene through semi-quantitative RT-PCR. The TcSERK gene expression was higher in embryogenic friable calli and lower in callus with early embryo induction. Flow cytometry analysis revealed that cells from ovary calli were haploids (1n = 10). This study would be a starting step for the induction of haploid embryogenesis from sliced ovaries of the well-adapted regional genotypes of cocoa, for obtaining rapid homozygosity; as induction of haploids through androgenesis in an earlier study could not yield fruitful results.

An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization

Awad, Noor H.,Ali, Mostafa Z.,Mallipeddi, Rammohan,Suganthan, Ponnuthurai N. Elsevier science 2018 Information Sciences Vol. No.

<P>Contemporary real-world optimization benchmarks are subject to many constraints and are often high-dimensional problems. Typically, such problems are expensive in terms of computational time and cost. Conventional constraint-based solvers that are used to tackle such problems require a considerable high budget of function evaluations. Such budget is not affordable in practice. In most cases, this number is considered the termination criterion in which the optimization process is stopped and then the best solution is marked. The algorithm might not converge even after consuming the pre-defined number of function evaluations, and hence it does not guarantee an optimal solution is found. Motivated by this consideration, this paper introduces an effective surrogate model to assist the differential evolution algorithm to generate competitive solutions during the search process. The proposed surrogate model uses a new adaptation scheme to adapt the theta parameter in the well-known Kriging model. This variable determines the correlation between the parameters of the optimization problem being solved. For that reason, an accurate surrogate model is crucial to have a noticeable enhancement during the search. The statistical information exploited from a covariance matrix is used to build the correlation matrix to adapt the theta variable instead of using a fixed value during the search. Hence, the surrogate model evolves over the generations to better model the basin of the search, as the population evolves. The model is implemented in the popular L-SHADE algorithm. Two benchmark sets: bound-constrained problems and real-world optimization problems are used to validate the performance of the proposed algorithm, namely iDEaSm. Also, two engineering design problems are solved: welded beam and pressure vessel. The performance of the proposed work is compared with other state-of-the-art algorithms and the simulation results indicate that the new technique can improve the performance to generate better statistical significance solutions. (C) 2018 Elsevier Inc. All rights reserved.</P>