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A novel distributed energy allocation mechanism for Distribution System Operator (DSO) market through a bi-level iterative auction is proposed. With the locational marginal price at the substation node known, the DSO runs an upper level auction with aggregators as intermediate agents competing for energy. This DSO level auction takes into account physical grid constraints such as line flows, transformer capacities and node voltage limits. This auction mechanism is a straightforward implementation of projected gradient descent on the social welfare (SW) of all home level agents. Aggregators, which serve home level agents - both buyers and sellers, implement lower level auctions in parallel, through proportional allocation and without asking for utility functions and generation capacities that are considered private information. The overall bi-level auction is shown to be efficient and weakly budget balanced., Published in IEEE transactions on Smart Grid

Grid computing has been treated as a new paradigm for solving large and complex scientific problems using resource sharing technique through many distributed administrative domains. The dynamic nature of Grid resources and the demands of users create challenge in the Grid scheduling problem that cannot be addressed by deterministic algorithms with polynomial time complexity. Thus, the use of meta-heuristic is more appropriate option in obtaining optimal results. The Genetic Algorithm (GA) has been proven as one of the best methods for Grid scheduling. The GA explores the problem space globally, but is sometimes unable to search locally. Thus, a hybrid algorithm is proposed which combines intelligently the GA with Particle Swarm Optimization (PSO) for the Grid job scheduling. The hybrid GA-PSO aims to reduce the schedule makespan and flowtime. The proposed hybrid algorithm is compared with the standard GA and PSO on both parameters. The comparison results exhibit that the proposed algorithm outperforms other two algorithms.

Grid computing has been used as a new paradigm for solving large and complex scientific problems using resource sharing mechanism through many distributed administrative domains. One of the most challenging issues in computational Grid is efficient scheduling of jobs, because of distributed heterogeneous nature of resources. In other words, the job scheduling in computational Grid is an NP-hard problem. Thus, the use of meta-heuristic is more appropriate option in obtaining optimal results. In this article, the authors propose a novel hybrid scheduling algorithm which combines intelligently the exploration ability of Particle Swarm Optimization (PSO) with the exploitation ability of Extremal Optimization (EO) which is a recently developed local-search heuristic method. The hybrid PSO-EO reduces the schedule makespan, processing cost, and job failure rate and improves resource utilization. The proposed hybrid algorithm is compared with the standard PSO, population-based EO (PEO) and standard Genetic Algorithm (GA) methods on all these parameters. The comparison results exhibit that the proposed algorithm outperforms other three algorithms.

Scheduling jobs in computational Grids is considered as NP-complete problem owing to the heterogeneity of shared resources. The resources belong to many distributed administrative domains that enforce various management policies. Therefore, the use of meta-heuristics are more appropriate option in obtaining optimal results. In this article, a novel hybrid population-based global optimization algorithm, called the Hybrid Firefly Algorithm and the Differential Evolution (HFA-DE), is proposed by combining the merits of both the Firefly Algorithm and Differential Evolution. The Firefly Algorithm and the Differential Evolution are executed in parallel to support information sharing amongst the population and thus enhance searching efficiency. The proposed HFA-DE algorithm reduces the schedule makespan, processing cost, and improves resource utilization. The HFA-DE is compared with the standard Firefly Algorithm, the Differential Evolution and the Particle Swarm Optimization algorithms on all these parameters. The comparison results exhibit that the proposed algorithm outperforms the other three algorithms.

Image enhancement can be formulated as an optimization problem where one parameterized transformation function is used for enhancement purpose. The proper enhancement significantly depends on two factors- fine tuning of the parameters of the corresponding parameterized transformation function and other one is the selection of a proper objective function. In this study a parameterized variant of histogram equalization (HE) has been used for enhancement purpose and to tune the parameters of that variant a modified cuckoo search (CS) with new global and local search strategies is employed. This paper also concentrates on the selection of a proper objective function to preserve the original brightness of the image. A new objective function has been developed by combining fractal dimension (FD) and quality index based on local variance (QILV). Visual analysis and experimental results prove that modified CS with search strategies outperforms the traditional and some other existing modified CS algorithms. Considering the image’s brightness preserving capability, the proposed objective function significantly outperforms other existing objective functions.

Nature-inspired optimization algorithms have proved their efficacy for solving highly nonlinear problems in science and engineering. Firefly Algorithm (FA), which is one of them, has been taken to be broadly discussed here. However, practical studies demonstrate that the proper setting of the FA’s parameters is the key difficulty. FA associates with some sensitive parameters, and proper setting of their values is extremely time-consuming. Therefore, parameter less variants of FA have been proposed in this study by incorporating the adaptive formulation of the control parameters which are free from the tuning procedure. Population size greatly influences the convergence of any nature-inspired optimization algorithm. Generally, lower population size enhances convergence speed but may trap into local optima. While larger population size maintains the diversity but slows down the convergence rate. Therefore, the crucial effect of different population size over FA’s efficiency has also been investigated here. The population size (of considered FA is varied within the size [10, 1280] where it gets doubled in each run starting with \(n = 10\). Therefore, eight instances of FA have been executed, and the best one is considered by the user over different classes of functions. Experimental results also show that the proposed parameter-less FA with a population size between 40 and 80 provides best results by considering optimization ability and consistency over any classes of functions.

Terrains with steep inclines cannot be utilized for crop production as it is hazardous to operate conventional farm equipment for various cropping tasks. This research, which is a part of a larger effort to deploy a team of robots for seeding and other similar tasks, proposes an approach for route planning of a team of ground mobile robots (drones) for seed delivery as well as an unmanned aerial vehicle (UAV) to continually replenish the drones with seeds. The overall cost is formulated in terms of linearly constrained integer quadratic programming (LCICQ). Using a Lagrangian function, a recurrent neural network with primal and dual sets of neurons is proposed to converge to optimal solutions.

This research describes an approach to implement a bi-level decentralized auction mechanism in transactive energy markets. At the lower level, a set of aggregators obtain energy demands of prosumers in their vicinity. The upper level is realized by the DSO, which maximizes the total utilities of all prosumers while maintaining prosumer privacy. The underlying constrained optimization problem is solved using the augmented Lagrangian method. Simulation results indicate that aggregators with higher demands and lower generations are charged at relatively higher rates. Furthermore, as aggregators that are further away from the DSO tend to constrain the grid more, their prosumers are also associated with higher energy costs.

Job scheduling is one of the major challenges in Grid computing systems to efficiently exploit the capabilities of dynamic, autonomous, heterogeneous and distributed resources for execution of different types of jobs. Thus optimal job scheduling is an NP-complete problem which can easily be solved by using heuristic techniques. This paper presents a hybrid algorithm for job scheduling using Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for efficiently allocating jobs to resources in a Grid system so that makespan and flowtime are minimized. This proposed algorithm combines the advantages of both GA and CSA. The authors' results have been compared with standard GA, CSA and Ant Colony Optimization (ACO) to show the importance of the proposed algorithm.

This paper investigates the problem of proportionally fair double-sided energy auction involving buying and selling agents. The grid is assumed to be operating under islanded mode. A distributed auction algorithm that can be implemented by an aggregator, as well as a possible approach by which the agents may approximate price anticipation is considered. Equilibrium conditions arising due to price anticipation is analyzed. A modified auction to mitigate the resulting loss in efficiency due to such behavior is suggested. This modified auction allows the aggregate social welfare of the agents to be arbitrarily close to that attainable with price taking agents. Next, equilibrium conditions when the aggregator collects a surcharge price per unit of energy traded is examined. A bi-objective optimization problem is identified that takes into account both the agents' social welfare as well as the aggregator's revenue from the surcharge. The results of extensive simulations, which corroborate the theoretical analysis, are reported.

Distribution locational marginal pricing (DLMP) can adversely affect users in a grid-constrained transactive distribution system market (DSM) that are at a distance away from the substation, requiring longer paths to connect to the substation. When the grid operates closer to its physical limits in terms of line capacities and voltage deviations, these users are more likely to cause grid violations than others in the vicinity of the substation. Conversely, increased energy consumption by users near the substation can choke off the supply to those at the grid s extremities. This research describes a novel mechanism to charge users in a more equitable manner, by regularizing the distribution system operator (DSO)s social welfare based objective function with a fairness criterion. The Jains index of fairness is used in this context and corresponding fairness DLMP component is derived. The overall problem entails the maximization of the regularized objective within a set of linear constraints. The constraints that ensure that the grids voltage and line power limits are not violated. Constrained optimization is carried out in an iterative manner through dual decomposition where the dual variables and unit costs are incrementally updated by the DSO using the augmented Lagrangian method (ALM). Simulations reported in this study confirm the effectiveness of the proposed approach.

Differential Evolution (DE) is a simple but powerful evolutionary algorithm. Crossover Rate (CR) and Mutation Factor (F) are the most important control parameters in DE. Mutation factor controls the diversification. In traditional DE algorithm CR and F are kept constant. In this paper, the values of CR and F are modified to enhance the capability of traditional DE algorithm. In the first modified algorithm chaotic sequence is used to perform this modification. In the next modified algorithm Lévy Flight with chaotic step size is used for such enhancement. In the second modified DE, population diversity has been used to build population in every generation. As a result the algorithm does not converge prematurely. Both modified algorithms have been applied to optimize parameters of the parameterized contrast stretching function. The algorithms are tested for satellite image contrast enhancement and the results are compared, which show that DE via chaotic Lévy and population diversity information outperforms the traditional and chaotic DE in the image enhancement domain.

Nature-inspired algorithms have been applied in the optimization field including digital image processing like image enhancement or segmentation. Firefly algorithm (FA) is one of the most powerful of them. In this paper two different implementation of FA has been taken into consideration. One of them is FA via levy flight where step length of levy flight has been taken from chaotic sequence. Chaotic sequence shows ergodicity property which helps in better searching. But in the second implementation chaotic sequence replaces levy flight to enhance the capability of FA. Population of individuals has been created in every generation using the information of population diversity. As an affect FA does not converges prematurely. These two modified FA algorithms have been applied to optimize parameters of parameterized contrast stretching function. Entropy, contrast and energy of the image have been used as objective criterion for measuring goodness of image enhancement. Fitness criterion has been maximized in order to get enhanced image with better contrast. From the experimental results it has been shown that FA with chaotic sequence and population diversity information outperforms the Particle swarm optimization and FA via levy flight.

In this paper, we study a day ahead double energy auction in a distribution system involving dispatchable generation units, renewable generation units supported by battery storage systems(BSSs), fixed loads, price responsive loads, and supply from the Whole Sale Market(WSM) at Locational Marginal Price(LMP). The auction is implemented within a Distribution System Operator (DSO) premises using Mixed Integer Linear Programming (MIP). The proposed auction is cleared at the Distribution LMP (DLMP) and is observed to be weakly budget balanced if no penalty is applied for DSO's deviation from originally committed supply from the WSM. Furthermore, the dynamics of LMP and DLMP, and their effect on distribution market participants scheduled quantities as well as the WSM supply to the distribution system is investigated., Comment: Electro Information Technology (EIT), 2017 IEEE International Conference on

The Swine Influenza Model Based Optimization (SIMBO) family is a newly introduced speedy optimization technique having the adaptive features in its mechanism. In this paper, the authors modified the SIMBO to make the algorithm further quicker. As the SIMBO family is faster, it is a better option for searching the basin. Thus, it is utilized in local searches in developing the proposed memetic algorithms (MAs). The MA has a faster speed compared to SIMBO with the balance in exploration and exploitation. So, MAs have small tradeoffs in convergence velocity for comprehensively optimizing the numerical standard benchmark test bed having functions with different properties. The utilization of SIMBO in the local searching is inherently the exploitation of better characteristics of the algorithms employed for the hybridization. The developed MA is applied to eliminate the power line interference (PLI) from the biomedical signal ECG with the use of adaptive filter whose weights are optimized by the MA. The inference signal required for adaptive filter is obtained using the selective reconstruction of ECG from the intrinsic mode functions (IMFs) of empirical mode decomposition (EMD).

This paper presents a novel, two-phase approach for optimal generation scheduling, taking into account the environmental issue of emission allowance trading in addition to the economic issue of operation cost. In the first phase, hourly-optimal scheduling is done to simultaneously minimize operation cost, emission, and transmission loss, while satisfying constraints such as power balance, spinning reserve and power generation limits. In the second phase, the minimum up/down time and ramp up/down rate constraints are considered, and a set of 24-h optimal schedules is obtained using the outputs of the first phase. Simulation results indicate effectiveness of the proposed approach.

HighlightsWe describe a prey-predator fishery model incorporating partial closure for the populations.The criterion for the existence of interior equilibrium point, local and global stability of the system are derived.The existence of bionomic equilibrium is discussed.The optimal system is derived and solved numerically using an iterative method with Runge-Kutta fourth-order scheme.Numerical simulations are carried out to verify the analytically proved results. A prey-predator type fishery model incorporating partial closure for the populations is described in this paper. The proposed model deals with a problem of non-selective harvesting of a prey-predator system in which both the prey and the predator species obey logistic law of growth. The predator dependent Beddington DeAngelis type functional response is taken into consideration. Dynamic behavior of the system is analyzed. Partial closure for the populations is considered as a controlling instrument to regulate the harvesting of the populations. A dynamic framework towards the optimal utilization of the resource is developed using Pontryagin's maximum principle. The optimal system is numerically solved using an iterative method with Runge-Kutta fourth order scheme. Simulation results show that the optimal control scheme can achieve sustainable ecosystem. Results are analyzed with the help of graphical illustrations.

Biogeography-based optimization (BBO) inherently lacks exploration capability that leads to slow convergence. To address this limitation, authors present a memetic algorithm (MA) named as aBBOmDE, which is a new variant of BBO. In aBBOmDE, the performance of BBO is accelerated with the help of a modified mutation and clear duplicate operators. Then modified DE (mDE) is embedded as a neighborhood search operator to improve the fitness from a predefined threshold. mDE is used with mutation operator DE/best/1/bin to explore the search near the best solution. The length of local search is a choice that balances between the search capability and the computational cost. In aBBOmDE, migration mechanism is kept same as that of BBO in order to maintain its exploitation ability. Modified operators are utilized to enhance the exploration ability while a neighborhood search operator further enhances the search capability of the algorithm. This combination significantly improves the convergence characteristics of the original algorithm. Extensive experiments have been carried out on forty benchmark functions to show the effectiveness of the proposed algorithm. The results have been compared with original BBO, DE, CMAES, other MA and DE/BBO, a hybrid version of DE and BBO. aBBOmDE is also applied to compute patch dimensions of rectangular microstrip patch antennas (MSAs) with various substrate thicknesses so as to be used a CAD formula for antenna design.

In power system reconfiguration, the status (ON/OFF) of switches are optimized such that maximum power is delivered to loads after the occurrence of a fault. The optimized reconfiguration is achieved by prioritizing power delivered to vital loads over semi-vital and nonvital loads. The formulation presented in this paper considers a new balanced hybrid (AC and DC) shipboard power system (SPS). Analysis of the nonconvex reconfiguration formulation is done by an appropriate nonconvex solver and by convex approximation. Unlike the nonconvex solution that is based on branch-and-bound methods, convex approximation significantly reduces complexity. It is shown that for the hybrid SPS reconfiguration problem, low complexity convex approximations are effective in finding optimal solutions. Cumulative distribution function (CDF) of the power delivered to loads is presented to showcase the system robustness against random fault scenarios. A combined objective of maximizing power delivery and minimizing the number of switching actions is included in the analysis. Tradeoff between power delivered and number of switching operations after reconfiguration has been discussed at steady state. A separate analysis is also included to observe the intermediate dynamic switch states while the reconfiguration is in progress to capture the trade-off more prominently.

This paper describes a prey–predator fishery model with stage structure for prey. The adult prey and predator populations are harvested in the proposed system. The dynamic behavior of the model system is discussed. It is observed that singularity induced bifurcation phenomenon is appeared when variation of the economic interest of harvesting is taken into account. We have incorporated state feedback controller to stabilize the model system in the case of positive economic interest. Fishing effort used to harvest the adult prey and predator populations is used as a control to develop a dynamic framework to investigate the optimal utilization of the resource, sustainability properties of the stock and the resource rent earned from the resource. Pontryagin’s maximum principle is used to characterize the optimal control. The optimal system is derived and then solved numerically using an iterative method with Runge–Kutta fourth-order scheme. Simulation results show that the optimal control scheme can achieve sustainable ecosystem.

Invasive Weed Optimization (IWO) was recently proposed as a simple but powerful metaheuristic algorithm for real parameter optimization. IWO draws inspiration from the ecological process of weeds colonization and distribution and is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. This article extends the basic IWO for tackling multi-objective optimization problems that aim at achieving two or more objectives (very often conflicting) simultaneously. The concept of fuzzy dominance has been used to sort the promising candidate solutions at each iteration. The new algorithm has been shown to be statistically significantly better than some state of the art existing evolutionary multi-objective algorithms, namely NSGAIILS, DECMOSA-SQP, MOEP, Clustering MOEA, GDE3, and MOEADGM on a 12-function test-suite (including both unconstrained and constrained problems) from the IEEE CEC (Congress on Evolutionary Computation) 2009 competition and special session on multi-objective optimization algorithms. The following performance metrics were considered: IGD, Spacing, and Minimum Spacing. Our experimental results suggest that IWO holds immense promise to appear as an efficient metaheuristic for multi-objective optimization.

In this paper the bacteria foraging meta-heuristic is extended into the domain of multiobjective optimization. In this multiobjective bacteria foraging (MOBF) optimization technique, during chemotaxis a set of intermediate bacteria positions are generated. Next, we use pareto non-dominance criterion to determine final set of bacteria positions, which constitute the superior solutions among current and intermediate solutions. To test the efficacy of our proposed algorithm we have chosen a highly constrained optimization problem namely economic/emission dispatch. Economic dispatch is a constrained optimization problem in power system to distribute the load demand among the committed generators economically. Now-a-days environmental concern that arises due to the operation of fossil fuel fired electric generators and global warming, transforms the classical economic load dispatch problem into multiobjective environmental/economic dispatch (EED). In the proposed work, we have considered the standard IEEE 30-bus six-generator test system on which several other multiobjective evolutionary algorithms are tested. We have also made a comparative study of the proposed algorithm with that of reported in the literature. Results show that the proposed algorithm is a capable candidate in solving the multiobjective economic emission load dispatch problem.

This paper proposes the bacterial foraging meta-heuristic algorithm for multiobjective optimization. In this multiobjective bacterial foraging optimization technique, the most recent bacterial locations are obtained by chemotaxis process. Next, Fuzzy dominance based sorting procedure is used here to select the Pareto optimal front (POF). To test the suitability of our proposed algorithm we have considered a highly constrained optimization problem namely economic/emission dispatch. Now-a-days environmental concern that arises due to the operation of fossil fuel fired electric generators and global warming, transforms the classical economic load dispatch problem into multiobjective environmental/economic dispatch (EED) problem. In the proposed work, we have considered the standard IEEE 30-bus six-generator test system and the results obtained by proposed algorithm are compared with the other recently reported results. Simulation results demonstrate that the proposed algorithm is a capable candidate in solving the multiobjective economic emission load dispatch problem.

Biogeography-Based Optimization (BBO) uses the idea of probabilistically sharing features between solutions based on the solutions’ fitness values. Therefore, its exploitation ability is good but it lacks in exploration ability. In this paper, the authors extend the original BBO and propose a hybrid version combined with ePSO (particle swarm optimization with extrapolation technique), namely eBBO, for unconstrained global numerical optimization problems in the continuous domain. eBBO combines the exploitation ability of BBO with the exploration ability of ePSO effectively, which can generate global optimum solutions. To validate the performance of eBBO, experiments have been conducted on 23 standard benchmark problems with a range of dimensions and diverse complexities and compared with original BBO and other versions of BBO in terms of the quality of the final solution and the convergence rate. Influence of population size and scalability study is also considered and results are compared with statistical paired t-test. Experimental analysis indicates that the proposed approach is effective and efficient and improves the exploration ability of BBO.

This paper describes genetic and hybrid approaches for multiobjective optimization using a numerical measure called fuzzy dominance. Fuzzy dominance is used when implementing tournament selection within the genetic algorithm (GA). In the hybrid version, it is also used to carry out a Nelder-Mead simplex-based local search. The proposed GA is shown to perform better than NSGA-II and SPEA-2 on standard benchmarks, as well as for the optimization of a genetic model for flowering time control in rice. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks. The hybrid version also compares well with ParEGO on a few other benchmarks. The proposed hybrid algorithm is then applied to estimate the parameters of an elaborate gene network model of flowering time control in Arabidopsis. Overall solution quality is quite good by biological standards. Tradeoffs are discussed between accuracy in gene activity levels versus in the plant traits that they influence. These tradeoffs suggest that data mining the Pareto front may be useful in bioinformatics.

This paper proposes two new algorithms based on the clonal selection principle for the design of spreading codes for DS-CDMA. The first algorithm follows a multi-objective approach, generating complex spreading codes with ''good'' auto as well as cross-correlation properties. It also enables spreading code design with no restrictions on the number of users or code length. The algorithm maintains a repertoire of codes that are subject to cloning and undergo a process of affinity maturation to obtain better codes. Results indicate that the produced code sets lie very close to the theoretical Pareto front. A second penalty function-based constrained optimization algorithm based on clonal selection is proposed. It is applied to the design of spreading codes with pre-defined power spectral density requirement. The results suggest that the algorithm is capable of lowering significantly, the power spectra at undesired frequencies. Therefore, with the proposed algorithm, a DS-CDMA transmitter can, for the first time, selectively transmit power across the transmission bandwidth and adjust to jammers and other interferers. This study illustrates that using two stages of multi-objective and constrained optimization, using the proposed clonal selection algorithms, is an effective code design strategy.

form only given. This paper proposes a hybrid algorithm based on artificial immune systems and ant colony optimization for distribution system reconfiguration, which is formulated as a multi-objective optimization problem. The algorithm maintains a population of candidate solutions called antibodies. The search space is explored by means of the hypermutation operator that perturbs existing antibodies to produce new ones. A table of pheromones is used to reinforce better edges during hypermutation. An added innovation is the use of the pheromones to obtain quick solutions to restore the distribution system under contingency situations. The hybrid approach has been successfully implemented on two test networks. The results obtained demonstrate the efficacy of the algorithm.

This paper proposes a double auction mechanism for energy trade between buying and selling agents. The framework is general enough, requiring neither the agents' preferences nor the energy pricing to be fixed values across the spatially distributed agents. A microgrid controller implements a distributed algorithm to maximize individual participating agents' utilities as well as the social welfare. This is accomplished by the controller in an iterative manner, such that the need to obtain private information pertaining to individual agents' preferences is obviated. Simulation results with a set of seven buyers and an equal number of sellers indicate that the proposed iterative double auction can establish social welfare maximization, requiring only a reasonable amount of computational overhead.

Due to loss of diversity of loads, the restoration of distribution feeders after long interruptions creates cold load pickup conditions. As a result, the total load briefly exceeds the substation transformer rated load. In order to prevent overheating of these transformers, the distribution system load may have to be restored in a step-by-step manner using sectionalizing switches. The restoration time is dependent on the order in which sections are restored. We propose genetic algorithms and ant colony algorithms as two stochastic optimization algorithms to compute the globally best restoration sequence of sections. Both approaches belong to a class of algorithms known as evolutionary algorithms. While genetic algorithms are well known techniques for optimization, ant colony algorithms have been proposed very recently. Results obtained using both methods for two test cases are presented.

Grid computing is a high performance distributed computing platform to solve complex and large-scale scientific problems. It consists of heterogeneous computing resources connected by a network across dynamic and geographically distributed organisations to create a distributed high performance computing infrastructure. Job scheduling in computational Grid is known as NP-complete problem owing to the problem complexity and intractable nature of the problem. Such a problem could be solved using heuristic algorithms. These types of algorithms have the ability to find a near optimal solution in reasonable time rather than the optimal solution in a very long processing time. The primary objective of the scheduling is to minimise the makespan of the system. In this paper, an Improved Cuckoo Search (ICS) optimisation method has been proposed for scheduling user-jobs to available resources so that various performance metrics are optimised. Here, our attention has been focused on the improvement of computational Grid performance in terms of makespan and completion time. The study reveals that the proposed ICS algorithm provides better results in comparison with Cuckoo Search (CS) and Simulated Annealing (SA).

Game theory applies mathematical models in deciding interactions among people and their outcome. Mixed strategy Nash equilibrium in general exists in every game with a finite set of actions. Correlated equilibrium is a new solution concept in game theory that is more general than Nash Equilibrium and can be formulated with more accuracy on solutions. In this paper a Correlated Equilibrium based control has been extended to an Islanded Microgrid structure which depicts that scenario in smart-grids when it is isolated from the main power supply during power scarcity. The game theory based control has been shown to perform exceedingly well for the islanded scenario considering multi-agent structure. The individual agents are segregated by a superagent as potential buyers and sellers.A novel problem formulation is proposed and a Constrained variant of a newly proposed Differential Evolution Algorithm, ADE-LbX has been used for solving the non linear optimization problem. The final outcome has shown that the overall utility or satisfaction of agents is maximized extensively after energy trading besides maintaining trade advantage for each agent.

The article deals with the problem of deployment and configuration of a given set of sensors on a synthetically generated 3-D terrain using evolutionary computation. It proposes a multi-objective formulation of this problem which aims at positioning and utilizing sensors in such a way that several conflicting objectives of our interest are optimized simultaneously and then this optimization problem is solved using a recent fuzzy dominance based decomposition technique for multi-objective optimization called MOEA/DFD. Simulation results not only validate the efficiency of multi-objective approach over single-objective approach solved with a Differential Evolution (DE) algorithm, but also justify the superiority of MOEA/DFD over the original MOEA/D-DE and another very well-known MO algorithm called NSGA-II (Non-dominated Sorting Genetic Algorithm).

This chapter focuses on concept of new variant of Bacterial Foraging Optimization (BFO) named as Parallel Bacterial Foraging Optimization (PBFO). The key issues on implementation of PBFO in parallel architecture are also addressed. PBFO and its fusions with Particle Swarm Optimization (PSO) and its variants to optimize multimodal functions with high dimensions are discussed. Fusion of PBFO with parameter free Particle Swarm Optimization (pf-PSO) is validated on unimodal and multimodal benchmark functions with high dimensions. The PBFO attains good quality solution as compared to BFO on mutimodal functions.

Differential Evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through the similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike the 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, which makes the scheme self-organizing in this respect. Scale Factor is a very important control parameter of DE. This article describes a very competitive yet very simple form of adaptation technique for tuning the scale factor, on the run, without any user intervention. The adaptation strategy is based on the objective function value of individuals in DE population. Comparison with the most competitive and expensive variants of DE over the well-known numerical benchmarks reflects the superiority of this simple parameter automation strategy in terms of accuracy, convergence speed, and robustness.

Theoretical analysis of the properties of evolutionary algorithms is very important to understand their search behaviors and to develop more efficient algorithms. This article investigates the convergence properties of a canonical Differential Evolution (DE) algorithm with DE/rand/1 type mutation and binomial crossover. For simplicity and to provide an insight into the heuristics of the algorithm, the analysis has been done by assuming a single-dimensional fitness function f(x) . The analysis is independent of the nature of the objective function as long as it remains real-valued and possesses an unique global optimum (it may have multiple local optima as well).

In this paper, we describe a multi-objective approach to determine an effective mitigation strategy during an outbreak of foot and mouth disease in cattle. The goal of the approach is to minimize the total number of infections. In addition, the direct costs must also be minimized as a secondary objective. The animals culled and vaccinations may also be minimized, as long as the number of infections is not compromised. Thus a three-tier hierarchy of objectives exists. A framework that considers objective hierarchies during a multi-objective optimization algorithm is suggested. Results show that this produces better results than a conventional multi-objective optimization approach.

Biogeography-Based Optimization (BBO) is a bio-inspired and population based optimization algorithm. This is mainly formulated to optimize functions of discrete variables. But the convergence of BBO to the optimum value is slow as it lacks in exploration ability. The proposed Accelerated Biogeography-Based Optimization (ABBO) technique is an improved version of BBO. In this paper, authors accelerated the original BBO to enhance the exploitation and exploration ability by modified mutation operator and clear duplicate operator. This significantly improves the convergence characteristics of the original algorithm. To validate the performance of ABBO, experiments have been conducted on unimodal and multimodal benchmark functions of discrete variables. The results shows excellent performance when compared with other modified BBOs and other optimization techniques like stud genetic algorithm (SGA) and ant colony optimization (ACO). The results are also analyzed by using two paired t- test.

This paper presents novel two-phase multi-objective evolutionary approaches for solving the optimal generation scheduling problem with environmental considerations. Two different multi-objective evolutionary algorithms (MOEA) based on Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Archived Multi-objective Simulated Annealing (AMOSA) are presented in the paper. In the first phase, this approach formulates the hourly optimal generation scheduling problem as a multi-objective optimization problem which simultaneously minimizes operation cost and emission, while satisfying constraints such as power balance, spinning reserve and power generation limits. Results of the first phase are compared and SPEA2, which provided better results, is used for the second phase to obtain the optimal schedules for the 24 hours. The minimum up/down time and ramp up/down rate constraints are incorporated in the second phase. A case study for a 10-unit test system is carried out to illustrate the application and the effectiveness of the proposed approach.

A new Optimization Technique named as Sliced Particle Swarm Optimization (SPSO) is proposed. It introduces the slicing of search space into rectangular slices. It gives complete solution in terms of reduction in the computational cost and tracking minutely each sliced search space. It introduces the momentum factor which restricts the particle in a sliced search space. Linearly decreasing inertia weight is applied throughout the computation process which balances between global exploration and local exploitation. The proposed algorithm is tested on three benchmark functions and the results are promising.

Real world optimization problems are often too complex to be solved through analytic means. Evolutionary algorithms are a class of algorithms that borrow paradigms from nature to address them. These are stochastic methods of optimization that maintain a population of individual solutions, which correspond to points in the search space of the problem. These algorithms have been immensely popular as they are derivativefree techniques, are not as prone to getting trapped in local minima, and can be tailored specifically to suit any given problem. The performance of evolutionary algorithms can be improved further by adding a local search component to them. The Nelder-Mead simplex algorithm (Nelder & Mead, 1965) is a simple local search algorithm that has been routinely applied to improve the search process in evolutionary algorithms, and such a strategy has met with great success. In this article, we provide an overview of the various strategies that have been adopted to hybridize two wellknown evolutionary algorithms - genetic algorithms (GA) and particle swarm optimization (PSO).

Biogeography-Based Optimization (BBO) is a new bio-inspired and population based optimization algorithm. The convergence of original BBO to the optimum value is slow. Intelligent Biogeography-Based Optimization (IBBO) technique is a hybrid version of BBO with Bacterial Foraging algorithm (BFA). In this paper, authors integrate the bacterial intelligence feature of BFA to decide the valid emigration in migration process of BBO. This increases the ability of BBO to escape from getting trapped in local minima and converges to near-global optimum. This makes faster convergence to the optimum value. To validate the performance of IBBO, experiments have been conducted on unimodal and multimodal benchmark functions of discrete variables. IBBO and BBO are initialized with integer versions of population.

This paper presents fusion of Bacterial Foraging with parameter free Particle Swarm Optimization (HBF-pfPSO). The proposed technique is used to enhance quality of global optima of multimodal functions. The authors propose two major modifications in Bacterial Foraging Optimization (BFO). Firstly, all bacteria position and direction are updated after all fitness evaluations instead of each fitness evaluation in chemotaxis step. In order to accelerate the global performance of BFO, the bacteria update their current positions by pfPSO called as mutation. Due to pfPSO, the proposed technique does not require any additional parameter and velocity equation for fine-tuning as bacteria positions are updated directly by local and global best positions. The experimental results on three bencmark functions validadte claims. The proposed technique attains good quality of optima as compared to other techniques on mutimodal functions while showing faster convergence.

This paper proposes fusion of Synchronous Germ Computing (SGC) with Twin Swarm Intelligence (TSI) technique named as SGCTSI to enhance quality of global solutions with faster convergence of multimodal functions. In this paper, initially the authors tried to increase the speed of bacteria by updating bacteria positions synchronously, which is treated as SGC. In SGC, all the bacteria update their positions to attain global best position after completion of each generation, by adopting the feature of communication with each other. After each generation, current positions of bacteria are updated by co-operation of ePSO (Particle Swarm Optimization with extrapolation technique) and GLBestPSO (Global and Local Best PSO) called as mutation operator. The mutation operator brings about diversity in the population to avoid premature convergence or being trapped in some local optima. The SGCTSI has more global search ability at the beginning and improves the quality of solutions at the end of each run. The proposed technique is tested with eight standard benchmark functions and results are compared with ePSO GLBestPSO and canonical PSO (cPSO). The experimental results on bencmark functions validadte that, the proposed trifusion approch produces good quality solution with faster convergence compared to other techniques. The performance of the SGCTSI has been tested through various statistical parameters and analysis of variance (ANOVA) test.

In this paper, a novel approach to optimally operate a day-ahead electricity market with considerations of market power is proposed. First, this approach formulates the 1-hour based optimal dispatch problem as a nonlinear constrained multi-objective optimization problem which simultaneously minimizes fuel cost, emissions and the modified Herfindahl-Hirschman index. Then the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve this optimization problem. Simulation results illustrate that this algorithm is capable of finding the Pareto-optimal front in a single run. The best operation schedule for the whole 24 hours can be chosen by taking the ramp rate constraints of generators into account. This approach can provide the market operator with much operating feasibility and thus is more realistic and feasible.

Point estimates of the parameters in real world models convey valuable information about the actual system. However, parameter comparisons and/or statistical inference requires determination of parameter space confidence regions in addition to point estimates. In most practical applications, the relation of the parameters to model fitness is highly nonlinear and noisy data leads to further deviations. Thus the confidence regions obtained by using locally linearized models are often misleading. Uniform covering by probabilistic rejection (UCPR) is a robust technique that has been developed to solve this problem, and has been proven to be more efficient than other approximate random search techniques. In this paper, we propose a contour particle swarm optimization (C-PSO) technique and compare its performance against UCPR in predicting the confidence regions. Results indicate that for problems with low number of parameters, both the algorithms are quite comparable. However, real world models such as genetic networks have a large number of parameters and the UCPR fails in finding good convergence due to its limited search capabilities. In such problems, the C-PSO technique was able to find the confidence regions with better resolution and efficiency.

This paper describes a PSO-Nelder Mead Simplex hybrid multi-objective optimization algorithm based on a numerical metric called µ -fuzzy dominance. Within each iteration of this approach, in addition to the position and velocity update of each particle using PSO, the k-means algorithm is applied to divide the population into smaller sized clusters. The Nelder-Mead simplex algorithm is used separately within each cluster for added local search. The proposed algorithm is shown to perform better than MOPSO on several test problems as well as for the optimization of a genetic model for flowering time control in Arabidopsis. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks.

Particle swarm optimization is a stochastic algorithm for optimizing continuous functions. It uses a population of particles that follow trajectories through the search space towards good optima. This paper proposes adding a local search component to PSO to improve its convergence speed. Two possible methods are discussed. The first adds a term containing estimated gradient information to the velocity of each particle. The second explicitly incorporates the Nelder-Mead algorithm, a known local search technique, within PSO. The suggested methods have been applied to the problem of estimating parameters of a gene network model. Results indicate the effectiveness of the proposed strategies.

In this paper, the parameters of a genetic network for rice flowering time control have been estimated using a multi-objective genetic algorithm approach. We have modified the recently introduced concept of fuzzy dominance to hybridize the well-known Nelder Mead Simplex algorithm for better exploitation with a multi-objective genetic algorithm. A co-evolutionary approach is proposed to adapt the fuzzy dominance parameters. Additional changes to the previous approach have also been incorporated here for faster convergence, including elitism. Our results suggest that this hybrid algorithm performs significantly better than NSGA-II, a standard algorithm for multi-objective optimization.

The paper proposes a novel evolutionary approach to spreading code design in DS-CDMA. Specifically, a multiobjective evolutionary algorithm is used to generate complex spreading sequences that are optimized with respect to average mean square cross correlation and/or autocorrelation properties. A theoretical model is developed in order to demonstrate the optimality of the generated codes. The proposed algorithm enables spreading code design with no constraints on the code length. Furthermore, it is possible to generate K/spl ges/N codes of length N with very little cost in correlation properties. This results in significant capacity enhancement in DS-CDMA systems.