Predator-prey Optimization Research Articles

Predator–prey optimization based clustering algorithm for wireless sensor networks

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.

A predator-prey optimization for structural health monitoring problems

Monitoring a structure using permanent sensors has been one of the most interesting topics, especially with the increase of the number of aging structures. Such a technique requires the implementation of sensors on a structure to predict the condition states of the structural elements. However, due to the costs of sensors, one must judiciously install few sensors at some defined locations in order to maximize the probability of detecting potential damages. In this paper, we propose a methodology based on a genetic algorithm of type predator-prey with a Bayesian updating of the structural parameters, to optimize the number and location of the sensors to be placed. This methodology takes into consideration all uncertainties related to the degradation of the elements, the mechanical model and the accuracy of sensors. Starting with two initial populations representing the damages (prey) and the sensors (predator), the genetic algorithm evolves both populations in order to converge towards the optimal configuration of sensors, in terms of number and location. The proposed methodology is illustrated by a two-story concrete frame structure.

Adaptive predator–prey optimization for tuning of infinite horizon LQR applied to vehicle suspension system

This paper puts forward an adaptive predator–prey optimization algorithm to solve the weight selection problem of linear quadratic control applied for vibration control of vehicle suspension system. The proposed technique addresses the two key issues of PSO, namely (a) the premature convergence of the particles, and (b) the imbalance between exploration and exploitation of the particles in finding the global optimum. The main principle behind this optimization algorithm is that the inertia weight is adaptively updated based on the success rate of the particles to increase the convergence, and the predator–prey strategy is reinforced to avoid the particles getting trapped in a local minimum thereby, guaranteeing convergence of the particles towards the global optimal solution. The convergence of the particles towards the global minimum is guaranteed on the basis of a passivity argument. Moreover, the strength of this new adaptive optimization technique to tune the gains of linear quadratic regulator is validated experimentally on a laboratory scale active vehicle suspension system for improved ride comfort and passenger safety.

Multiobjective thermal power load dispatch using adaptive predator–prey optimization

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.

Design of Digital IIR Filter with Conflicting Objectives Using Hybrid Predator–Prey Optimization

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.

Hydro-thermal generation scheduling using integrated gravitational search algorithm and predator–prey optimization technique

In this research work, an integrated optimization technique has been proposed by coordinating gravitational search algorithm (GSA) and predator–prey optimization (PPO) in a suitable manner to improve the search capability of algorithm. The integrated technique is applied to obtain the optimum generation schedule of hydro-thermal generation system considering some of the practical constraints and transmission losses. For the hydro-thermal systems, the multi-chain hydro model has been undertaken with due consideration of water transport delay between reservoirs. In PPO algorithm, the search is performed by considering the experience of other prey particles along with the effect of predator particle. The predator effect helps to avoid any possible stagnation of global best prey on local optima due to the fear created by predator particle. In PPO algorithm, the quality of the solutions has not been considered while updating the position of prey or predator, whereas in GSA, the agent direction is computed based on the overall force, and it is proportional to the quality of the solutions. Further, GSA is memory less and agent direction is not influenced by best positions. In the proposed integrated technique, the position of agent/prey is directed by overall force around themselves, global best prey position and predator effect. The proposed integrated technique is tested on three hydro-thermal systems. A penalty-free constraint handling approach is employed to satisfy all equality and inequality constraints. The results obtained from proposed technique have been compared with the results reported with the existing technique, and it is experienced that proposed technique is able to provide a better solution with improved convergence characteristics. The statistical analysis of results is also done to measure the sensitivity and robustness of the proposed technique.

A bio-inspired novel optimization technique for reactive power flow

In the arena of power system operation and planning, the optimal reactive power flow (ORPF) plays a pivotal role, wherein the application of classical techniques poses issues in obtaining the optimal solutions and hence is usually employed with the meta-heuristic and/or bio-inspired techniques, with a view to converge swiftly towards an optimal solution. Usually, ORPF can have uneven, intermittent objectives and multi-constraint functions; and such intricacies of ORPF can best be suppressed by employing a combination of nature-inspired algorithms as a process of hybridization. Thus, in this paper, an approach has been endeavoured to hybridize the Biogeography based optimization (BBO) with that of the predator-prey optimization (PPO), so as to be rightfully termed as “adaptive biogeography based predator-prey optimization” (ABPPO). In such a way, this paper elucidates a novel hybrid technique that includes adaptive mutation combined with predator-prey pattern for attaining the global optimal point. In adaptive mutation scheme, the diversity measure of distance-to-average point is the predominant feature that dodges the supremacy of extremely feasible solutions throughout enhancing the population diversity. The predators explore around the elite prey in a determined way, whereas the preys search the solution space so as to evade from the predators. This tool improves the utilization and searching abilities of the BBO exploration procedure, thereby offers a mean of evading from the suboptimal point and imposes the populace to attain at the global best point. The efficacy of this hybrid scheme is validated against the standard test cases of IEEE-30 and IEEE-57 bus systems. The results show the efficiency and vitality of the proposed method.

Integrated search technique for parameter determination of SVM for speech recognition

Support vector machine (SVM) has a good application prospect for speech recognition problems; still optimum parameter selection is a vital issue for it. To improve the learning ability of SVM, a method for searching the optimal parameters based on integration of predator prey optimization (PPO) and Hooke-Jeeves method has been proposed. In PPO technique, population consists of prey and predator particles. The prey particles search the optimum solution and predator always attacks the global best prey particle. The solution obtained by PPO is further improved by applying Hooke-Jeeves method. Proposed method is applied to recognize isolated words in a Hindi speech database and also to recognize words in a benchmark database TI-20 in clean and noisy environment. A recognition rate of 81.5% for Hindi database and 92.2% for TI-20 database has been achieved using proposed technique.

An improved SVM using predator prey optimization and Hooke-Jeeves method for speech recognition

For automatic speech recognition, speech signal is represented in terms of featureset. Size of the feature set is an important aspect as it affects recognition accuracy,computational time, memory requirement and complexity of the model. Supportvector machine has good application prospects for speech recognition; nevertheless,performance of support vector machine is affected by its parameters. In this researchwork, a hybrid optimization technique is proposed to improve the learning abilityof support vector machine and to select the most appropriate feature set. The hybridtechnique integrates predator-prey optimization and Hooke-Jeeves method. To dealwith mixed type of decision variables, binary predator-prey optimization technique hasalso been introduced. During the initial phase, search is performed by predator-preyoptimization and to further exploit the search, Hooke-Jeeves method is applied. Theproposed technique with support vector machine has been implemented to recognizeTI-46 isolated word database in clean as well as noisy conditions and self-recordedHindi numeral database. The experimental results obtained by proposed techniquewith support vector machine shows improved recognition rate. Furthermore ROCcurve is analysed to verify sensitivity and specificity of results obtained by proposedtechnique with support vector machine.

Synergic predator-prey optimization for economic thermal power dispatch problem

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.

Weight pattern evaluation for multiobjective hydrothermal generation scheduling using hybrid search technique

This paper presents best weight pattern evaluation approach to solve short-term multi-objective hydrothermal generation scheduling (HTGS) which determines the allocation of power demand among the committed generating units, to minimize operating cost and minimal impacts on environment subjected to physical and technological constraints. A multi-chain interconnected hydro system having non-linear relationship between water discharge rate and power generation is undertaken with due consideration of water transport delay between connected reservoirs. The best weights are computed by conventional statistical measures, which characterize the correlation coefficients matrix evolution. The solution methodology hybridizes global and local search techniques. Predator-prey optimization (PPO) is undertaken as a global search technique and Powell’s pattern search (PPS) is exploited as a local search technique. The results among the competing objectives obtained by the proposed method are compared with various results reported in the literature. The sensitivity and robustness of the proposed technique are evaluated by performing statistical analysis of results obtained based on independent runs. The integration of PPO and PPS improves the quality of solution and convergence characteristics.

Adaptive biogeography based predator–prey optimization technique for optimal power flow

This article presents a new approach based on a hybrid algorithm consisting of biogeography based optimization (BBO) with an adaptive mutation scheme and the concept of predator–prey optimization technique for solving the multi-objective optimal power flow problems. The adaptive mutation scheme, based on distance-to-average point diversity measure, avoids the dominance of highly probable solutions through increasing the population diversity. The predators search around the best prey in a concentrated manner, while the preys explore the solution space so as to stay away from the predators. These mechanisms enhance the exploitation and exploration capabilities of the BBO search process, provide a mean of escaping from the suboptimal solutions and force the population to arrive at the global best solution. The proposed method is tested on IEEE 30 bus test system with different objectives that reflect fuel cost minimization, loss reduction, voltage profile improvement and voltage stability enhancement. The comparison of results with those of the existing approaches illustrates the effectiveness and robustness of the suggested method.

Multiobjective fixed head hydrothermal scheduling using integrated predator-prey optimization and Powell search method

An integrated methodology that embeds global and local search technique is explored for solving multiobjective hydrothermal scheduling. Predator-prey optimization (PPO) is used as a base level search in the global search space and Powell's method as a local search technique. Predator-prey model is based on particle swarm optimization (PSO). In spite of several advantages of PSO as compared to other evolutionary methods, it has some major drawbacks such as local optimal trapping and lack of efficient capability to treat the constraints. PPO model includes predator and prey particles in population. The prey objective is to escape from the predators reaching to its lair, while predator's goal is to capture the prey that increase exploration and exploitation capability of PSO. Powell's method aims to fine tune the solution obtained from PPO and to enhance the search capability of proposed method. In addition, a penalty less constraint handling method is employed for considering equality and inequality constraints. Fuzzy methodology has also been exploited to find the best compromised solution. The proposed integrated technique is applied to three hydrothermal scheduling problems with valve-point effect of thermal generators and transmission losses. The proposed method yields high quality solution while satisfying all constraints.

Multi-objective Short-term Hydrothermal Generation Scheduling Using Predator–Prey Optimization

In this article, a predator–prey-based optimization technique is applied to obtain the scheduling of a hydrothermal system with cascaded reservoirs, minimizing economic and gaseous pollutants and emission objectives. These objectives are mutually conflicting and are equally important. Predator–prey optimization is a stochastic optimization technique based on the particle swarm optimization concept having an additional predator effect that helps to explore the search area more efficiently due to the fear created by the predator. A heuristic search technique is applied for generating an initial feasible solution. The direct substitution method is implemented to handle the equality constraints, whereby dependent variables are determined from the equality constraint. Inequality constraints of dependent variables are taken care by incorporating an additional objective function represented by the fuzzy membership index, and other variables are set to their limits on violation. Fuzzy methodology has been exploited for solving a decision-making problem involving the multiplicity of objectives and selection criterion for the best compromised solution. The solutions obtained from the proposed technique are compared with other existing techniques, and results are found to be satisfactory. The proposed method has the capability to escape from local optimum solutions due to its predator effect, and it is easy to implement.

Predator prey optimization for snake‐based contour detection

PurposeThe purpose of this paper is to describe a work that aims to solve contour detection problem using a planar deformable model and a swarm‐based optimization technique. Contour detection is an important task in image processing as it allows depicting boundaries of objects in an image. The proposed approach uses snakes as active contour model and adapts predator prey optimization (PPO) metaheuristic so that to define a new dynamic for evolving snakes in a way to reduce time complexity while providing good quality results.Design/methodology/approachIn the proposed approach, contour detection has been cast as an optimization problem requiring function minimization. PPO has been used to develop a search strategy to handle the optimization process. PPO is a population‐based method inspired by the phenomenon of predators attack and preys evasion. It has been proposed as an improvement of particle swarm optimization (PSO) where additional particles are introduced to repel the other particles into the swarm. The introduced dynamic is intended to achieve better exploration of the search space. In the design, a representation scheme has been first defined. Each particle either a predator or a prey is represented as a curve (snake) defined by a set of control points. The idea is then to evolve a set of curves using the dynamic governed by PPO model equations. As a result, the curve that optimizes a defined energy function is identified as the contour of the target object.FindingsApplication of the proposed method to a variety of images using a multi agent platform has shown that good quality results have been obtained compared to a PSO‐based method.Originality/valueNature inspired computing is an emergent paradigm that witnesses a growing interest because it suggests a new philosophy to optimization. This work contributes in showing its suitability to solve problems even it is still at infancy. In another hand, despite the amount of work done in image processing, it is still required to define new methods for image segmentation. This work outlines a new way to deal with this problem through the use of PPO.