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In this paper, an advanced modus operandi named the -constrained simplex method ( ACSM ) is deployed to resolve a real-time hydro-thermal-wind-solar power scheduling problem. ACSM is an updated articulation of the Nonlinear Simplex Method ( SM ) of Nelder and Mead. It has been designed after interbreeding an ordinary SM with some other methods like-evolutionary method, α-constrained method, etc. To develop this technique three alterations in the SM are adopted (i) -level differentiation, (ii) mutations of the worst point, and (iii) the incorporation of multi-simplexes. A real-time multi-objective hydro-thermal-wind-solar power scheduling problem is established and optimized for the Kanyakumari (Tamil Nadu, India) for the 18 th of September of 2020. Four contrary constraints are contemplated for this case study (i)fuel cost and employing cost of wind and solar power system, (ii) NOx emission, (iii) SO2 emission, and (iv) CO2 emission. The fidelity of the projected practice is trailed upon two test systems. The first test system is hinged upon twenty-four-hour power scheduling of a pure thermal power system. The values of total fuel cost, emission, emission, and emission are attained as 4707.19 $/day , 59325.23 kg/day, 207672.70 kg/day, and 561369.20 kg/day, respectively. In the second test system, two thermal generators are reintegrated with renewable energy resources (RER) based power system (hydro, wind, and solar system) for the same power demands. The hydro, wind, and solar data are probed with the Glimn-Kirchmayer model, Weibull Distribution Density Factor, and Normal Distribution model, respectively. The outturns using ACSM are contrasted with the SM and evolutionary method( EM ). For this real-time hydro-thermal-wind-solar power scheduling problem the values of fuel cost, emission, emission, and emission are shortened to 1626.41 $/day , 24262.24 kg/day, 71753.80 kg/day, and 196748.20 kg/day, respectively for the specified interval using ACSM and with SM , these values are calculated as 1626.57 $/day , 24264.67 kg/day, 71760.98 kg/day, 196767.68 kg/day, respectively. The results for the same are obtained as 1626.74 $/day , 24267.10 kg/day, 71768.15 kg/day, 196787.55 kg/day, respectively, by using EM . The values of the operating cost of the solar system, wind system, total system transmission losses, and computational time of test system-2 with ACSM , SM , and EM are evaluated as 8438.76 $/day , 19017.42 $/day , 476.69 MW/day & 15.6 seconds; 8439.61 $/day , 19019.33 $/day , 476.74 MW/day and 16.8 sec; and 8447.20 $/day , 19036.43 $/day , 477.17 MW/day and 17.3 sec, respectively. The solutions portray the sovereignty of ACSM over the other two methods in the entire process .

Previous studies are focused on the cogeneration unit commitment problem (CUCP) to find optimum operating cost with satisfaction of operation constraints. However, the profit and environment emissi...

Meta-heuristic techniques are gaining attractiveness to solve constrained economic load dispatch problems due to higher global solution capacity, flexibility and derivative free structure. In this ...

An improved particle swarm optimization integrated with a simplex search method (MPSO_SSM) is suggested as an efficient technique applied to economic-emission power dispatch (EEPD) problems. The particle swarm optimization (PSO) is a stochastic population search approach to locate a global solution in the exploration area that is refined by adding the local search using a simplex search method (SSM) which is a deterministic approach. Conversion of multiobjective economic-emission optimization problem into single objective is done by using price penalty factors, which reduces the complex time-consuming calculations to produce a feasible generating schedule. The variable elimination method is applied to handle equality constraints and further, if the dependent generator violates the generation limits, the penalty method is applied to it. Various benchmark functions and small-, medium- and large-scale systems are considered to check the robustness of the suggested approach. The results obtained compete with other optimization algorithms stated in the literature.

No single metaheuristic search algorithm can be adjudged universally best general-purpose optimizer. The performance of search algorithms mainly depends upon the weightage assigned to global and lo...

In this paper, multi-objective economic-environmental solar-wind-thermal power scheduling model was developed and it was optimized for five test systems. First test system was based upon a purely thermal power generating system and its problem was formulated to satisfy three conflicting objectives: (i) fuel cost, (ii) emission, and (iii) emission. The second, third and fourth test systems were comprised of optimal scheduling of integrated solar-thermal, wind-thermal and solar-wind-thermal power systems, respectively. Uncertainty costs were also considered in the renewable power based systems. These four test systems were examined for five power demands i.e. 200 MW, 225 MW, 250 MW, 275 MW, & 300 MW. Fifth test system was also deployed upon a renewable-thermal power scheduling. The effects of variation in number of thermal generators on fuel cost and emission were perceived, for a power demand of 400 MW. The values of fuel cost (4067.98 Rs/h) and emission (2,441.05 kg/h) reduced to 3,232.94 Rs/h and 1,939.30 kg/h, respectively, when number of thermal generators were reduced from four to two. The -constrained simplex method (ACSM) was used for simulation and the results were compared with simplex method (SM). The results clearly depict the dominance of ACSM over SM in almost all the fields.

This paper presents the solution of economic power dispatch (EPD) in thermal power plants using the hybridization of particle swarm optimization (PSO) and simplex search method (SSM). EPD is obtaining the best generating schedule to supply the power demand and covering the transmission losses with minimum overall fuel cost. Physical constraints like valve point loading effects, ramp rate limits and prohibited operating zones are also included with basic EPD problem to increase the practicability in the problem. As PSO performs well in finding the global best solution and SSM in finding the local best solution, thus their combination improves the overall minimum results obtained for the generation fuel cost objective function. The performance of the proposed methodology is tested on different test systems having categories of small-scale, medium-scale and large-scale power system problems. The results obtained are then compared with other reported methods to show the superiority of the proposed algorithm.

The unit commitment (UC) problem is formulated by undertaking dual operating mode combined heat and power (CHP) units along with thermal and heat units. The extraction and backpressure mode of the CHP unit provides the facility to adopt immediate change in the generation, which depends upon the ratio of the heat and power generation. The impact of dual-mode CHP (DM-CHP) unit has been examined on multi-objective (MO) economic and profit based models, in order to deal with pollutant emission along with cost/profit. The model of CHP-UC problem is a mixed integer model, hence to deal with binary and real variables, binary particle swarm optimization (BPSO) and PSO technique have been employed, respectively. To demonstrate the effectiveness of the optimization technique, two test systems of thermal UC problem have been undertaken and the results are compared. In order to verify the benefits of DM-CHP unit, three test systems of CHP-UC problem have been undertaken and the impact of backpressure and extraction mode of CHP unit has been investigated in terms of cost/profit and emission. It has been verified from the results that backpressure and extraction mode of the CHP unit provide diverse UC status and generation schedule.

Owing to no free lunch theorem, meta-heuristic search algorithms, behaviour and performance are different for solving specific class of optimization problems than another. Further, search algorithms are conceptualized of exploration and exploitation aspects. Ameliorated grey wolf optimization algorithm is proposed to solve economic power load dispatch problem that synergizes between exploration and exploitation aspects. Ameliorated grey wolf optimization procedure coordinates the behaviour of grey wolves, random exploratory search, local random search and opposition learning heuristics. Grey wolf optimization algorithm is considered to perform global search and random exploratory search applied to perform a search in the neighbourhood of already visited search locations. To maintain good solutions and diversity among solutions, opposition based learning has been implemented. The practical aspects i.e. value-point loading effect, avoidance of prohibited operating zones and ramp rate limits, generation limits of generators and satisfaction of power demand constraint are undertaken to solve economic power load dispatch problem. Heuristic search is exploited to handle equality constraints. The proposed algorithm is validated on standard benchmark functions and medium to large electric power systems. Results reveal that proposed technique is a potential method to solve economic load dispatch problems as it competes with recent algorithms undertaken for comparison.

Grouping the sensor nodes into clusters is an effective way to organize wireless sensor networks and to prolong the networks’ lifetime. This paper presents a static clustering algorithm that employs predator–prey optimization (PPO) for identifying cluster heads as well as routes for sending data to the sink. The objective of the optimization algorithm is to reduce the energy consumed in data collection and transmission, to achieve equalization in energy utilization by the wireless sensor nodes and to prolong the wireless sensor network lifetime while avoiding the expenses of cluster reformation in each communication round. The novelty of this algorithm is to treat the identification of cluster heads and the choice of transmission paths a unified optimization problem of minimizing the total energy cost of the network, whereas existing algorithms consider them two separate optimization sub-problems. PPO algorithm is applied to select the most appropriate pair of cluster heads for each cluster. It also identifies the optimum communication path, which can be single or multiple hop. The energy consumed in data transmission is reduced and a uniformity in residual energy of the nodes is achieved. The performance of the novel algorithm has been evaluated by observing the patterns in which nodes consume their energies. The number of packets that are successfully delivered has been found to be better than the existing static clustering algorithms, and at par with the finest dynamic clustering algorithms.

In this paper, a nature inspired algorithm which is based on krill herd behavior is coupled with a local search technique. The proposed local search based krill herd algorithm (LSKHA) is implemented on non-convex economic dispatch problem having five generating units. This proposed algorithm allows the up-gradation of position of krill with selected individuals only instead of randomly picked individuals and it must cause the prevention of trapping of optimal solution in local minima. The variation of fuel cost is shown with respect to number of trails and optimal solution is observed with number of iterations too. The optimal solution obtained is compared with a recently implemented optimization technique in terms of fuel cost, transmission losses and statistical measures. The proposed technique has shown satisfactory improvement in optimal solution based on the result analysis.

While meeting the growing power needs of the world, use of fossil fuels has increased so much that the crises of their looming has raised now. Along with that, global warming and air pollution are also the matters of great worries for humans. All these problems can be reduced by enhancement of utilization of renewable energy resources in power generation. In many parts of the world, wind energy is present in sufficient quantity. Just one percent of wind power in the world is equivalent to 2 X 1011 W, which is about three percent of total world energy consumption rate. Role of wind energy in power generation field is increasing day by day because evolved technologies and power management techniques have started suppressing its shortcomings. In this paper, multi-objective wind– thermal power scheduling problem has been proposed and it is optimized by using α-constrained simplex method to satisfy economic and emission (NO x ,) SO 2 &CO 2 ) constraints. The credibility of presented method has been corroborated on five test systems.

Sun is the biggest source of renewable energy, which is sustainable, inexhaustible, readily available, free of cost, non-polluting and most abundant. Earth and its atmosphere are continuously accepting about 1.7 X 1017 W solar radiations. If only one percent of these radiations can be transformed into electrical energy with 10 percent efficiency, then all the energy needs of the whole world can be fulfilled. A few years ago, it wasn’t used more frequently as an electrical power source because of its unreliability. Now modern technology has changed the picture to a great extent. Solar PV has become the most competitive choice for power generation in increasing number of sites for residential and commercial purposes. Many countries have inducted ample solar power into their electrical grids to reduce their dependence on fossil fuels. In this paper, economic- environmental, solar-thermal power scheduling problem of an integrated system is recommended and it is optimized by using α - constrained simplex method, which is composed after inserting α -constrained method to nonlinear simplex method along with mutations. Justifiability of the presented method has been tested on five test systems.

A hybrid artificial algae algorithm (HAAA) has been proposed in this paper that hybridizes the artificial algae algorithm (AAA) and simplex search method (SSM) to solve economic load dispatch problem. The AAA simulates the life cycle of microalgae which comprises helical movement, evolutionary and adaptation phases. The helical movement phase provides the exploration while the evolutionary and adaptation phases provide the exploitation of search space. The exploration provided by the helical movement completely depends upon energy and shear-force parameters. In the proposed algorithm, the AAA acts as global optimizer while SSM provides local search. The SSM improves the exploitation capability by performing a local search. Dynamic tuning of parameters enhances the exploration capability of the proposed method. An iterative heuristic repair algorithm is applied to handle the equality constraint of economic load dispatch problem. Further, the operation in prohibited operating zones is avoided heuristically. The performance of HAAA is evaluated with CEC’05 standard test functions and small, medium and large-scale economic load dispatch problems. The simulation results advocate that the proposed algorithm produces better or comparable results than the well-established and popular algorithms. Finally, the Wilcoxon sign rank test is performed to validate the results.

In this work, we have undertaken a dual-mode combined heat and power unit commitment model to address the optimum generation scheduling of the committed unit. The model formulation includes the backpressure and an extraction mode of cogeneration unit, which is having a diverse feasible operating region of operation. In backpressure mode, the energy generated from the combined heat and power unit depends on a fixed ratio of power and heat, while in extraction mode, this ratio is relatively relaxed that provides system flexibility as compared to other. Owing to the dynamic features of different generating units as well as combination of binary and continuous decision variables, the problem becomes mixed-integer nondeterministic polynomial hard optimization problem, hence an integrated optimization technique has been applied. The integrated optimization technique is a combination of binary successive approximation technique and civilized swarm optimization technique. The binary successive approximation as a local search technique is used to update the unit status, iteratively and global search technique ‘civilized swarm optimization’ are applied to search optimal generation schedule from the committed units. In order to measure the impact of a dual-mode combined heat and power unit on optimum generation scheduling and system flexibility, four test systems have been undertaken. It has been observed from results that total operating cost of the system during extraction mode of combined heat and power unit is less as compared to backpressure mode. It is also evident from the results, that system provides diverse flexibility in terms of power and heat capability, during the extraction and backpressure mode of operation.

Unit commitment problem with huge number of constraints is considered as an important optimization problem encountered in the electric power systems. This paper proposes a modified binary different...

This paper presents an adaptive predator–prey optimization (APPO) to solve multiobjective thermal power dispatch problem considering objectives of operating cost and pollutant emission. In APPO the fear factor from a predator is a function of cognitive and social behavior of prey. It ensures continuous mobility (magnitude and direction) of prey, resulting in better diversification of solutions. The velocity of prey is maintained in the limits by recognizing the reinforcement and inhibition aspects. The multiobjective optimization problem is handled by weighting method, whereby the weight pattern assigned to the objectives has been undertaken as a decision variable. This results in non-inferior solutions at each swarm move. In order to select a best-compromised solution, the fuzzy theory is used. The performance of the proposed algorithm is investigated on six power system test problems. The proposed method provides better results in terms of lesser fuel cost and pollutant emission. The better satisfaction level of conflicting objectives, well distributed Pareto front, acceptable solution in a single trial run and insensitivity to parameter variations are observed in comparison to other existing methods reported in the literature.

Clustering of nodes is often used in wireless sensor networks to achieve data aggregation and reduce the number of nodes transmitting the data to the sink. This paper proposes a novel dual head static clustering algorithm (DHSCA) to equalise energy consumption by the sensor nodes and increase the wireless sensor network lifetime. Nodes are divided into static clusters based on their location to avoid the overhead of cluster re-formation in dynamic clustering. Two nodes in each cluster, selected on the basis of the their residual energy and their distance from the sink and other nodes in the cluster, are designated as cluster heads, one for data aggregation and the other for data transmission. This reduces energy consumption during intra-cluster and inter-cluster communication. A multi-hop technique avoiding the hot-spot problem is used to transmit the data to the sink. Experiments to observe the energy consumption patterns of the nodes and the fraction of packets successfully delivered using the DHSCA suggest improvements in energy consumption equalisation, which, in turn, enhances the lifetime of the network. The algorithm is shown to outperform all the other static clustering algorithms, while being comparable with the performance of the best dynamic algorithm.

A hybrid optimization technique based on the integration of binary successive approach (BSA) and civilized swarm optimization (CSO) has been proposed to solve profit based unit commitment (PBUC) problem. Since, PBUC is a mixed integer problem, hence to deal with binary and continuous variables, BSA and CSO technique has been employed, respectively. The BSA is based on evolutionary search and search process is initiated with random base point of the hypercube. The each base point further generates two more corner points of the hypercube. The search moves toward the point having better objective function value, and continues until the search has reached to the last branch of BSA tree. This strategy reduces the computational burden while searching the optimal unit status. The generation schedule from the committed unit is searched by CSO technique. The CSO is an integrated technique of PSO and society civilized algorithm (SCA) technique. Since, PSO has good exploration capability and SCA technique is emerging to improve the exploitation capability of the algorithm. Three PBUC test systems have been undertaken and obtained results have been compared with published results and found satisfactory. Further, Wilcoxon signed rank test is applied to investigate statistical performance of the proposed technique.

Environmentally constrained profit based unit commitment problem (PBUCP) is a combinatorial multi-objective optimization problem experienced in deregulated power systems, which optimizes ON/OFF status of generating units to maximize economic benefits with less environmental impacts. The PBUCP has gained less attention in the multi-objective framework. The Kyoto Protocol and various incentive policies have forced generation companies to consider emission minimization as an objective function while solving the PBUCP. In most of the recent studies in the literature, emissions are considered as a constraint, not as an objective function. A few studies have considered the emissions as an objective and suggested the compromised solutions of the multi-objective PBUCP. A synergy of the binary differential evolution (BDE) algorithm and a binary local search optimizer (BLSO) is proposed in the present research to solve the multi-objective PBUCP. A novel BLSO is proposed which deals with binary variables while exploiting the local search and is implemented on the commitment part of the multi-objective PBUCP. The proposed BLSO makes perturbations in the unit status based on the priority of units to refine the optimal solution searched by the BDE algorithm. The efficacy of proposed algorithms has been investigated on the small, medium and large power systems. A comparison with previously known best solutions is performed for the validation of obtained results. Computed results of proposed algorithms are dominant than the already published algorithms applied to solve the multi-objective PBUCP.

The multi-objective thermal power dispatch (MTPLD) problem allocates power demand among the committed generating units while minimizing the operating cost and pollutant's emission objectives simultaneously, subject to physical and technological constraints. The success to find MTPLD solution relies on the decision-maker's ability to select compromised solution under uncertain/diverse conditions and the search algorithm's potential to generate non-dominated solutions. Owing to ambiguous choice of objectives, this paper presents a fuzzy surrogate worth trade-off approach to decide the preferred solution among the non-dominated solution set. The uniformity of non-dominated solution set is maintained exploiting a quality measure approach. This paper presents a novel foraging activity paradigm to generate non-dominated solutions. The presented approach comprises of fly and walk behavior of preys. The direction of turn heuristic of prey handles system's equality constraint. The search algorithm balances exploration and exploitation and handles system constraints autonomously. The performance of proposed algorithm is investigated using generalized benchmark functions and multi-fuel, medium and large power system MTPLD problems. The experimental results shows that the proposed approach is robust, depends on least parameters and retains Pareto quality in independent trials while generating non-dominated solutions in a trial run.

The lifetime of a wireless sensor network (WSN) is a critical aspect as, in most of the applications, it is not possible to replace or recharge the batteries of the sensor nodes. The lifetime of a WSN with redundant deployment can be significantly increased by dividing the sensors into disjoint sets such that each of these sets, when operated independently, provides complete coverage of the targets. In order to maximize the lifetime of the network, the maximum possible number of such sets needs to be created. The problem has been proved to be nondeterministic polynomial complete. In this paper, a hybrid approach based on combining particle swarm optimization (PSO) with random transition moves has been proposed to address this problem. A swarm of randomly initialized particles explores the entire solution space in search of an optimum solution. Three novel random transition moves have been designed to exploit the redundancy in deployment of sensors and used to guide the randomly scattered particles towards the potential optimum solutions in their neighborhood. The transition moves escalate the convergence of the algorithm. The proposed algorithm has been tested both for point coverage and area coverage applications. To authenticate and validate the results, the comparison of the results is performed with the latest existing techniques. The proposed algorithm always finds the optimum solution by making fewer fitness function evaluations. The sensitivity analysis of the control parameters has also been performed.

The design of digital IIR filter as a single-objective optimization problem using evolutionary algorithms has gained much attention in the previous years. In this paper, the design of filter is treated as a multi-objective problem by simultaneously minimizing the magnitude response error, linear phase response error and optimal order within the stability constraints. The global search technique, predator–prey optimization (PPO), has been applied to design the digital IIR filter. The global search technique has been hybridized with binary successive approximation (BSA)-based evolutionary search method for exploring the search space locally. The relative performance of PPO and hybrid PPO has been evaluated by applying these techniques to standard mathematical test functions. The above-proposed hybrid search technique has been applied to achieve the solution for multi-parameter and multi-objective optimization problem of low-pass (LP), high-pass (HP), band-pass (BP) and band-stop (BS) digital IIR filter design. The results obtained from the proposed technique are compared with the results of other algorithms applied by other researchers for the design of digital IIR filter.

PSEUDO code of proposed technique.Display Omitted An optimization technique that embeds CSO and Powells search (PS) method is proposed.The proposed technique is applied to solve combined heat and power dispatch problem.CSO is having attributes of PSO and society civilization algorithm.Initially, search is performed by CSO and then PS is applied to improve the solution.The test systems having valve point loading effect and POZs constraint. An integrated technique that embeds civilized swarm optimization (CSO) and Powells pattern search (PPS) method is proposed to search economic dispatch of combined heat and power (CHP) dispatch problem. In the proposed technique, CSO is selected as global search technique and PPS is undertaken as a local search technique. Civilized swarm optimization is having attributes of particle swarm optimization (PSO) and society civilization algorithm (SCA). In CSO, mutually interacting societies forms the civilization. The positions of society particles are updated through the guidance of own leader along with their best positions. The best performing particle of CSO is further improved by PPS method based on a certain set criterion. The PPS method is based on the conjugate search direction method and does not require the gradient or Hessian matrix of the function to be optimized. The CHP dispatch problem has a mutual dependency of demand and heat-power capacity of generating units, so it requires an effective constraint handling strategy. In this work, variable reduction strategy with exterior penalty method is applied to satisfy equality constraints. The proposed technique is tested on five CHP test systems considering valve-point loading effect, prohibited operating zones constraint, and transmission losses. The obtained results are compared to the results reported in the literature and found satisfactory. Further, for verification of statistical performance of the proposed technique, t-test and Wilcoxon signed rank test is also performed.

In this work, a chaotic differential evolutionary and Powell’s pattern search (CDEPS) algorithm is proposed to solve multi-objective thermal power load dispatch (MTPLD) problem. The chaotic differential evolutionary method is responsible for the diversification, and Powell’s pattern search is dedicated to exploitation. Further, the performance of two CDEPS variants based on Gauss map and Tent map is investigated. The proposed MTPLD solution procedure either identifies a solution close to Pareto front or diversifies the existing Pareto frontier and finally selects a suitable compromising solution among the available options. In order to select the best compromising solution, a combination of surrogate worth trade-off approach and fuzzy theory is proposed as choice of objectives is ambiguous. The uniformity of Pareto front is evaluated by exploiting a quality measure approach. The performance analysis is done using generalized benchmark test functions and complex MTPLD problems. The ability of CDEPS to diversify Pareto front is verified by uniformity analysis of Pareto front. The one-sample Wilcoxon’s test and two-sample Mann Whitney’s test are used to analyze the experimental results. The exhaustive analysis shows that the Tent map-based CDEPS has better ability to generate quality generation schedule with uniform Pareto front quality and faster convergence rate.

This paper proposes an opposition-based greedy heuristic search (OGHS) strategy to solve multi-objective thermal power dispatch problem as a non-linear constrained optimization problem considering operating cost and pollutant emissions as competing objectives. The optimization problem is solved to find global solution, in case any one objective function is non-convex and non-differentiable. To generate initial population opposition-based learning is applied to select good candidates by exploring the search space extensively. Further, opposition-based learning is exploited for migration to maintain the diversity in the set of feasible solutions. Proposed method applies mutation strategy by perturbing the genes heuristically and seeking better one. This concept introduces parallelism and makes the algorithm always greedy for better solution. The greediness and randomness pulls the algorithm towards the global solution. The algorithm is also self sufficient without the need of tuning any parameter that effects acceleration of the algorithm. Fuzzy-theory is employed for decision-making that selects best solution from available non-inferior solutions. Feasible solution is also achieved heuristically that modifies the generation-schedule and avoids violation of operating generation limits. Proposed method has been implemented to analyze economic and multi-objective thermal power dispatch problems considering ramp-rate limits, prohibited-operating-zones, valve-point-loading effects, multiple-fuel options, environmental effects, and exact transmission losses encountered in realistic power system operation. The validity of proposed method is demonstrated on medium and large power systems. Proposed optimization technique is emerged out to compete with existing solution techniques. Wilcoxon signed-rank test for independent samples also proves the supremacy of proposed algorithm OGHS.

Synergic PPO optimization algorithm is proposed to solve ELD problems.The SPPO blends the psychology with prey's behavior to avoid predator.Small, medium and large size test power system are simulated.Multi-fuel test power system with transmission losses is also tested.Newton-Raphson procedure is applied to obtain transmission losses. This paper introduces a synergic predator-prey optimization (SPPO) algorithm to solve economic load dispatch (ELD) problem for thermal units with practical aspects. The basic PPO model comprises prey and predator as essential components. SPPO uses collaborative decision for movement and direction of prey and maintains diversity in the swarm due to fear factor of predator, which acts as the baffled state of preys' mind. In the SPPO, the decision making of prey is bifurcated into corroborative and impeded parts. It comprises four behaviors namely inertial, cognitive, collective swarm intelligence, and prey's individual and neighborhood concern of predator. The prey particle memorizes its best and not-best positions as experiences. In this research work, to improve the quality of prey swarm, which influence convergence rate, opposition based initialization is used. To verify robustness of proposed algorithm general benchmark problems and small, medium, and large power generation test power system are simulated. These test systems have non-linear behavior due to multi-fuel options and practical constraints. The constraints of prohibited operating zone and ramp rate limits of power generators' are handled using heuristics. Newton-Raphson procedure is exploited to attain the transmission losses using load flow analysis. The outcomes of SPPO are compared with the results described in literature and are found satisfactory.

Harmony Search (HS) is a population based metaheuristics search algorithm inspired from the musical process of searching for a perfect state of harmony and has ability to escape from local minima, does not require differential gradients and initial value setting for the variables and free from divergence and has strong ability on exploring the regions of solution space in a reasonable time. However, it has lower exploitation ability in later period and it easily trapped into local optima and converges very slowly. To improve the exploitation ability of HS algorithm in later stage and provide global optimal solution, a memetic algorithm approach considering Harmony Search and Random Search is presented in the proposed research to solve unit commitment problem of electric power system. The proposed memetic algorithm is tested for IEEE benchmarks consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is tested for unimodal and multimodal benchmark functions and is compared with others well known evolutionary, heuristics and meta-heuristics search algorithms and it has been found that performance of proposed hybrid algorithm is much better than classical Harmony Search Algorithm and Improved Harmony Search Algorithm as well as recently developed algorithms.

This paper proposes an integrated optimization technique which combines the features of the cat swarm optimization (CSO) algorithm with the traditional differential evolution (DE) algorithm and applies it for the optimal design of digital infinite impulse response (IIR) filters. Traditional design methods treat the digital IIR filter design as a single-objective optimization problem by taking into account the minimization of magnitude response error only and lack in considering the linear phase response error and the order of the filter. The aim of this paper was to design an IIR filter in multi-objective framework by equally considering the minimization of magnitude response error, the linear phase response error and the order of the filter. Firstly, the CSO algorithm is applied for digital IIR filter design. In order to start with a better solution set, the opposition-based learning strategy is then incorporated. To further improve the performance of CSO for designing stable digital IIR filters, the DE optimization algorithm is combined with CSO hence producing an integrated algorithm called multi-objective cat swarm and differential evolution algorithm (MOCSO-DE) which has the capability to explore and exploit the solution search space locally as well as globally. The developed integrated algorithm is effectively applied for the designing of the digital IIR low-pass (LP), high-pass (HP), band-pass (BP) and band-stop (BS) filters. To evaluate the effectiveness of the developed integrated algorithm, the computational results are compared with some well-established algorithms and it is observed that the developed algorithm is superior or at least comparable to other algorithms in getting better magnitude response and linear phase response together with lowest filter order and can also be implemented for the higher-order filter designs.

This article proposes a simple opposition-based greedy heuristic search to solve a dynamic thermal power dispatch problem as a non-linear constrained optimization problem in the constrained search space. Opposition-based learning is applied at two stages. First, an initial population is generated to select good candidates by extensively exploring the search space. Second, it is implemented for migration to maintain diversity in the set of feasible solutions. The proposed method applies a mutation strategy by perturbing the genes heuristically and seeking a better one, which introduces parallelism and makes the algorithm greedy for a better solution. The greediness and randomness pulls the algorithm toward a global solution. Acceleration of the algorithm is independent of any parameter tuning. Feasible solutions are achieved heuristically by modifying the generation schedules within operating generation limits. Opposition-based greedy heuristic search has been implemented to analyze dynamic economi...

Recent power system networks are characterized by large proportions, high interconnections, and high nonlinearities. Challenge of supplying the nation with high-quality, reliable electrical energy at a reasonable cost converted government policy into deregulation and restructuring environment. To achieve significant cost-savings, multiarea unit commitment strategies are employed, whose intention is to establish the optimal commitment stratagem for power generating units situated in numerous areas which are interconnected through tie-lines, and combined operation of generation resources can result in considerable operational cost-savings. Differential evolution is a population-based stochastic search algorithm, whose simple yet powerful and straightforward features make it gorgeous for optimization. Differential evolution uses somewhat greedy and less stochastic approach for optimization problem solution. Although global exploration ability of differential evolution (DE) algorithm is adequate, its local exploitation ability is feeble and convergence velocity is too low, and it suffers from the problem of untimely convergence for multimodal objective function, in which search process may be trapped in local optima and it loses its diversity. Also, it suffers from the stagnation problem, where the search process may infrequently stop proceeding toward the global optimum even though the population has not converged to a local optimum or any other point. To improve the exploitation ability and global performance of DE algorithm, a novel and hybrid version of differential evolution algorithm combined with random search algorithm is presented in the proposed research to solve multiobjective and multiarea unit commitment problem of electric power system. The performance of the proposed hybrid algorithm is tested with benchmark of three-area interconnected system, which consist of IEEE-30 Bus system. Experimental results show that proposed technique has the prospective for the solution of multiobjective and multiarea unit commitment problem and power generation scheduling in deregulated electricity market with import and export constraints.

This paper proposes a novel chaotic-crisscross differential evolution (CCDE) algorithm to realize an optimal generation schedule of multi-chain short-term hydrothermal system over 24 hours’ time-horizon in a multi-objective framework, considering conflicting economic-environmental aspects of thermal units. The equality constraints of active power balance and the amount of available water are independently handled using variable elimination method. However, the statistical uncertainties called residues arise due to infringements of equality constraints while adjusting the violated dependent variables within their boundaries. These residues are fuzzy quantified within their prescribed bounds, and are embedded as objectives to be optimized. An interactive unified fuzzy satisfying function is aimed to solve the conflict of three objectives. The global solution accuracy and convergence rate of stochastic algorithms are significantly affected by parameter-tuning, exploration and exploitation strategies. The proposed algorithm integrates dual crisscross mechanism orthogonally with chaotically tuned DE to balance exploration and exploitation. Information collected about non-dominated solutions from search space is processed using opposition-based learning for better accuracy of global solution in three-dimensional objective function hyperspace. The numerical results show improvement in unified satisfying objective function and convergence performance metrics over the existing methods. The competence of the proposed algorithm is confirmed through illustrations on benchmark functions and is substantiated through statistical significance tests.

This paper presents an emended salp swarm algorithm (ESSA) which is basically the extension of the basic salp swarm algorithm (SSA) to solve multiobjective electric power load dispatch problem. The main inspiration behind ESSA is the swarming behavior and reproduction cycle of salps. Salps, in the chain, move across a multi-dimensional search space to aim for the food source (global solution). Owing to the searching behavior of SSA that makes the algorithm prone to premature convergence, the solitary and colonial reproduction phase of salp has been introduced to improve the convergence behavior along with their swarming behavior The multiobjective optimization problem is firstly converted into scalar objective exploiting fuzzy set theory and the conflicting nature of objectives is resolved by cardinal priority ranking. The variable elimination method with exterior penalty is used to handle the physical and operational constraints of generating units. The validation of the proposed ESSA has been examined on the standard benchmark functions and seven EcLD test systems including both scalar and multiple objectives. The statistical analysis based on Wilcoxon sign rank test, supports that results achieved by the algorithm are superior to the other competing algorithms. So, the proposed algorithm (ESSA) is found a promising algorithm.

Unit commitment problem (UCP) and profit based unit commitment problem (PBUCP) are two combinatorial optimization problems with several constraints in the power system optimization research. In nowadays, the PBUCP becomes more dominant over the UCP due to restructuring in power industry. In recent years, many researchers have used various evolutionary algorithms to solve the multi-constrained PBUCP but differential evolution (DE) algorithm has received less consideration. A binary differential evolution (BDE) algorithm is introduced in the present research for solving the PBUCP. Further, to enhance the performance of the proposed BDE algorithm, a synergy of the BDE algorithm and a hill climbing local search optimizer (HCLSO) is used to solve the PBUCP. In the proposed synergy, BDE algorithm explores global aspect and a HCLSO method exploits local aspect. A HCLSO is proposed in this paper which makes perturbations in unit status based on a priority of units. A new de-commitment procedure is adopted to de-commit the needlessly committed units. The competency of the proposed BDE algorithm and BDE-HCLSO algorithm has been investigated on 10-units power system for 24 hours scheduling period. Results are justified by performing a comparison with the earlier reported algorithms employed to solve the PBUCP. Further, the simulation results demonstrate that BDE-HCLSO algorithm gives superior results over BDE algorithm.

The design of digital IIR filter design by using evolutionary algorithms has gained much attention in the previous years. Most of the researchers treated the design problem as a single objective optimization problem and applied the techniques for minimizing the magnitude response error. In this paper the design of filter is treated as a multi-objective problem by simultaneously minimizing the magnitude response error, linear phase response error and optimal order along with meeting the stability criterion. A hybrid heuristic search technique having differential evolution (DE) method as a global search technique and binary successive approximation based evolutionary search method as a local search technique has been proposed. Based on mean value of population, new mutation strategies have been proposed. The above proposed hybrid heuristic search technique has been applied effectively to solve the multi-parameter and multi-objective optimization problem of low-pass, high-pass, band-pass and band-stop digital IIR filter design. The obtained results reveal that the proposed technique with new proposed mutation strategies performs better than the already existing mutation strategies of DE and other algorithms applied by other researchers for the design of digital IIR filter.

In this paper, a population-based robust enhanced teaching--learning-based optimization (ETLBO) algorithm with reduced computational effort and high consistency is applied to design stable digital infinite-impulse response (IIR) filters in a multiobjective framework. Furthermore, a decision-making methodology based on fuzzy set theory is applied to handle nonlinear and multimodal design problems of the IIR digital filter. The original teaching--learning-based optimization (TLBO) algorithm has been remodeled by merging the concepts of opposition-based learning and migration for the selection of good candidates and to maintain diversity, respectively. A multiobjective IIR digital filter design problem takes into consideration magnitude and phase response of the filter simultaneously, while satisfying stability constraints on the coefficients of the filter. The order of the filter is controlled by a control gene whose value is also along with filter coefficients, to obtain the optimum order of the designed IIR filter. Results illustrate that ETLBO is more capable and efficient in comparison to other optimization methods for the design of all types of filter, i.e. high-pass, low-pass, band-stop, and band-pass IIR digital filters.

Harmony search (HS) is a population-based metaheuristics search algorithm inspired from the musical process of searching for a perfect state of harmony. The pitch of each musical instrument determines the aesthetic quality, just as the fitness function value determines the quality of decision variables. In the musical improvisation process, all players sound pitches within possible range together to make one harmony. If all the pitches make a good harmony, each player stores in his memory that experience and the possibility of making a good harmony is increased next time. Even though HS has the ability to escape from local minima, it does not require differential gradients and initial value setting for the variables, is free from divergence and has strong ability to explore the regions of solution space in a reasonable time. However, it has lower exploitation ability in later period and it easily trapped into local optima and converges very slowly. To improve the exploitation ability of HS algorithm in later stage and provide global optimal solution, a novel and hybrid version of harmony search combined with random search algorithm is presented in the proposed research to solve single-area unit commitment problem of electric power system. The proposed algorithm is tested for standard IEEE systems consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is compared with other well-known evolutionary, heuristics and metaheuristics search algorithms, and it has been found that performance of proposed algorithm is much better than of classical harmony search algorithm and improved harmony search algorithm as well as recently developed algorithms. Sensitivity analysis on proposed algorithm shows that low value of pitch adjustment rate results in better cost, and parametric test on proposed algorithm shows the rejection of the null hypothesis at the alpha significance level.

Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic search algorithm inspired by grey wolves (Canis lupus), which simulate the social stratum and hunting mechanism of grey wolves in nature and based on three main steps of hunting: searching for prey, encircling prey and attacking prey. This paper presents the application of GWO algorithm for the solution of non-convex and dynamic economic load dispatch problem (ELDP) of electric power system. The performance of GWO is tested for ELDP of small-, medium- and large-scale power systems, and the results are verified by a comparative study with lambda iteration method, Particle Swarm Optimization algorithm, Genetic Algorithm, Biogeography-Based Optimization, Differential Evolution algorithm, pattern search algorithm, NN-EPSO, FEP, CEP, IFEP and MFEP. Comparative results show that the GWO algorithm is able to provide very competitive results compared to other well-known conventional, heuristics and meta-heuristics search algorithms.

In this paper a new Hybrid Artificial Algae Algorithm (HAAA) is presented. This algorithm is developed by hybridizing artificial algae algorithm and simplex search method. To check the validity of HAAA, seven Unimodal and five multimodal benchmark functions taken from cec'05 is test. The proposed HAAA is also applied to solve 40 generator economic load dispatch problem to check its real world problem solving capability. The results obtained by HAAA shows that it is a robust algorithm and may be an efficient optimizer for many other real world optimization problems.

Due to its simple concept, easy understanding, and coding, the ion-motion optimizer has become an attracting optimizer for researchers to solve real-world optimization problems. However, it is likely to suffer from premature convergence as it performs optimization only on the behalf of distance between best cation and anions or best anion and cations. To overcome this drawback, this paper proposes a conglomerated optimizer based on ion-motion and crisscross search optimizers to solve non-linear, discontinuous and constrained electric power load dispatch problem. In the proposed conglomerated optimizer, the liquid phase of ion-motion optimizer has been modified to improve the exploration of search space and the exploitation has been improved by the crisscross search. The performance of the proposed conglomerated optimizer has been investigated on both unconstrained benchmark functions and six variants of electric power load dispatch problem considering various practical constraints. The equality constraint of electric power load dispatch problem has been handled by a heuristic repair algorithm based on proportionate power sharing. The operation in prohibited operating zones has been avoided using a heuristic technique. The qualitative analysis of the results obtained by the proposed optimizer has been done by a Wilcoxon sign-rank test. The comparison of results in terms of minimum, average, maximum and standard deviation with the existing and recently published state-of-art optimizers reveals that the proposed conglomerated optimizer is a promising candidate for both unconstrained benchmark functions and electric power load dispatch problem problems.

Profit-based unit-commitment problem (PBUCP) is a notable combinatorial optimizing problem faced in the deregulated power industry. The PBUCP finds the best profitable solution by committing and scheduling the thermal generating units efficiently. To solve the PBUCP, a new memetic binary differential evolution algorithm is proposed which considers binary differential evolution (BDE) algorithm as global search operator to improve the exploration aspect and binary hill-climbing (BHC) algorithm as local search operator to improve the exploitation aspect. A binary differential evolution algorithm is introduced whereby a new mutation strategy is implemented. A novel BHC algorithm makes priority-based perturbations on unit’s status to improve the global best solution searched by the BDE algorithm alone. A new excessive unit de-commitment strategy based on priority and total profit is also proposed. The power to committed units is allocated based on priority of units. The efficacy of algorithms has been researched on the PBUCP test systems comprising of 10-, 40- and 100-units over a time horizon. The outcomes of the proposed algorithms are compared with previously known best solutions. Simulated outcomes achieved by the proposed algorithms compete with the already reported algorithms to solve the PBUCP. Wilcoxon signed-rank test proves the predominance of the proposed algorithms statistically.

Day to day growing demand of electric energy, insufficiency of energy generation resources and increasing thermal power generation cost necessitates economic thermal power generation scheduling. Emission pollutants are required to be reduced as stringent regulations has been enforced to protect environment. In this article, interactive fuzzy technique is applied to solve the real and reactive power load dispatch of thermal units to improve the economy of generation and minimal impacts of emission on environment. Weighting method is employed to convert the economic-environmental electric power load dispatch (EPLD) problem into a scalar objective optimization problem. Weights are simulated for the generation of non-inferior solutions and trade-off function is established between the conflicting objectives. To achieve the ‘best weights pattern’ regression analysis is carried out between maximum satisfaction level of the participating objectives and trade-off function. Transmission losses are obtained by performing load flow analysis. The effectiveness of the propound approach is verified on a system, consisting of 10-bus, 13 lines and thermal generators-3.

In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm which has proven to be more effective than other population based algorithms. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where designing of band-pass filter is carried out. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.