Variable Search Research Articles

Determination of photovoltaic modules parameters at different operating conditions using a novel bird mating optimizer approach

The main goal of this paper is to provide a framework to accurately estimate the electrical equivalent circuit parameters of photovoltaic arrays by use of an efficient heuristic technique. Owing to the non-linearity of the current vs. voltage (I–V) characteristics of PV modules, using a superior optimization technique helps to effectively find the real electrical parameters. Inspired by the mating process of different bird species, bird mating optimizer (BMO) is a new invented search technique which has shown superior performance for solving complex optimization problems. In this paper, the original BMO algorithm is simplified and used to estimate the electrical parameters of the module model for an amorphous silicon PV system at different operating conditions. The simplified BMO (SBMO) eliminates tedious efforts of parameter setting in original BMO and also modifies some rules. The usefulness of the proposed algorithm is investigated by comparing the obtained results with those found by two particle swarm optimization (PSO) variants, two harmony search (HS) variants as well as seeker optimization algorithm (SOA). Based on the investigated situations of this paper, SBMO yields more accurate results than the other studied methods.

Solving the maximally diverse grouping problem by skewed general variable neighborhood search

The maximally diverse grouping problem requires finding a partition of a given set of elements into a fixed number of mutually disjoint subsets (or groups) in order to maximize the overall diversity between elements of the same group. In this paper we develop a new variant of variable neighborhood search for solving the problem. The extensive computational results show that our new heuristic significantly outperforms the current state of the art. Moreover, the best known solutions have been improved on 531 out of 540 test instances from the literature.

A Lagrangian decomposition approach for the pump scheduling problem in water networks

Dynamic pricing has become a common form of electricity tariff, where the price of electricity varies in real time based on the realized electricity supply and demand. Hence, optimizing industrial operations to benefit from periods with low electricity prices is vital to maximizing the benefits of dynamic pricing. In the case of water networks, energy consumed by pumping is a substantial cost for water utilities, and optimizing pump schedules to accommodate for the changing price of energy while ensuring a continuous supply of water is essential. In this paper, a Mixed-Integer Non-linear Programming (MINLP) formulation of the optimal pump scheduling problem is presented. Due to the non-linearities, the typical size of water networks, and the discretization of the planning horizon, the problem is not solvable within reasonable time using standard optimization software. We present a Lagrangian decomposition approach that exploits the structure of the problem leading to smaller problems that are solved independently. The Lagrangian decomposition is coupled with a simulation-based, improved limited discrepancy search algorithm that is capable of finding high quality feasible solutions. The proposed approach finds solutions with guaranteed upper and lower bounds. These solutions are compared to those found by a mixed-integer linear programming approach, which uses a piecewise-linearization of the non-linear constraints to find a global optimal solution of the relaxation. Numerical testing is conducted on two real water networks and the results illustrate the significant costs savings due to optimizing pump schedules.

On local search in d.c. optimization problems

First, we consider a d.c. minimization problem with a simple feasible set and develop a special method based on the linearization with respect to the basic nonconvexity. The convergence of the methods is analyzed and compared with published results. Theoretical and practical stopping criteria are proposed. Second, we consider a problem with d.c. constraint and study the properties of special local search method for this problem. Finally, we consider a variant of local search for a general d.c. optimization problem and investigate its convergence.

A general variable neighborhood search variants for the travelling salesman problem with draft limits

In this paper, we present two General Variable Neighborhood Search for the Traveling Salesman Problem with Draft Limits (TSPDL), a recent variant of the Traveling Salesman Problem. TSPDL arises in the context of maritime transportation. It consists of finding optimal Hamiltonian tour for a given ship which has to visit and deliver products to a set of ports while respecting the draft limit constraints. The proposed methods are tested on a set of benchmarks from the literature and computational experiments show their efficiency.

A two-stage no-wait job shop scheduling problem by using a neuro-evolutionary variable neighborhood search

A two-stage order scheduling problem with an additional no-wait condition is studied in this article. All orders must be processed from start to completion without any waiting between operations and any interruption within operations. Examples of such a problem occur in steel pro- duction and pharmaceutical, chemical, and construction in- dustries. A new optimization method which is based on a combination of an enhanced variable neighborhood search (VNS) and an artificial neural network (ANN) is devised in this paper. The aim is to simultaneously employ the key characteristicsoftwoapproachestoachieve superiorsolutions in solving the addressed optimization problem. In proposed algorithm, the VNS performs a global search whereas the ANNplaystheroleofintensiveinitialsolution.Theindividual obtained by feed forward back-propagation ANN is improved within enhanced VNS iterations. The two techniques comple- ment each other when ANN feeds its best solution to the VNS algorithm.Furthermore,toestablishthebestsequenceoforder pairs,ateamprocessalgorithmisalsoembeddedintotheVNS to enhance the search diversification as well as its intensifica- tion. The performance of proposed neuro-evolutionary algo- rithm is investigated via computational experiments, and the results prove the appropriate performance of suggested VNS- ANN algorithm.

Optimal Thrust Allocation for Semisubmersible Oil Rig Platforms Using Improved Harmony Search Algorithm

Deep-water offshore drilling vessels, such as a semisubmersible drilling rig, use the dynamic positioning (DP) system and the thruster-assisted position-mooring system for maintaining a stationary position. In the DP system, the thrust allocator is used to distribute the desired generalized forces computed by the motion controller among the thrusters. However, to ensure safe operation of the vessel despite the thruster failure, the vessel is equipped with redundant thruster configuration and, therefore, is overactuated. For overactuated vessels, the choice of a particular solution for thrust allocation is found using some kind of optimization process. In this paper, the thrust allocator tries to minimize the power consumption and takes forbidden/spoil zones into account. The formulated thrust allocation problem becomes nonconvex due to thrust direction constraints on azimuth thrusters. The conventional methods get trapped in local minima and fail to find the optimum solution for the formulated nonconvex thrust allocation problem. In this paper, an improved harmony search (IHS) algorithm for solving the nonconvex thrust allocation problem is proposed. IHS is a variant of the harmony search (HS) algorithm. The HS algorithm is a music-based meta-heuristic optimization method, which is analogous with the music improvisation process where a musician continues to polish the pitches to obtain better harmony. Obtained numerical results show that the IHS algorithm has better convergence property when compared to the HS algorithm and the genetic algorithm (GA). Moreover, the power consumption for thrust allocation using the IHS algorithm is lower when compared with HS, GA, and Mincon (sequential quadratic programming) algorithms. The percentage savings in total power consumption for thruster allocation as compared to the Mincon algorithm for GA, HS, and IHS methods are 44.96%, 48.39%, and 51.58%, respectively.

A new multi-objective particle swarm optimizer using empirical movement and diversified search strategies

Most real-world optimization problems involve the optimization task of more than a single objective function and, therefore, require a great amount of computational effort as the solution procedure is designed to anchor multiple compromised optimal solutions. Abundant multi-objective evolutionary algorithms (MOEAs) for multi-objective optimization have appeared in the literature over the past two decades. In this article, a new proposal by means of particle swarm optimization is addressed for solving multi-objective optimization problems. The proposed algorithm is constructed based on the concept of Pareto dominance, taking both the diversified search and empirical movement strategies into account. The proposed particle swarm MOEA with these two strategies is thus dubbed the empirical-movement diversified-search multi-objective particle swarm optimizer (EMDS-MOPSO). Its performance is assessed in terms of a suite of standard benchmark functions taken from the literature and compared to other four state-of-the-art MOEAs. The computational results demonstrate that the proposed algorithm shows great promise in solving multi-objective optimization problems.

On the parameterized complexity of consensus clustering

Given a collection C of partitions of a base set S, the NP-hard Consensus Clustering problem asks for a partition of S which has a total Mirkin distance of at most t to the partitions in C, where t is a nonnegative integer. We present a parameterized algorithm for Consensus Clustering with running time O(4.24k⋅k3+|C|⋅|S|2), where k:=t/|C| is the average Mirkin distance of the solution partition to the partitions of C. Furthermore, we strengthen previous hardness results for Consensus Clustering, showing that Consensus Clustering remains NP-hard even when all input partitions contain at most two subsets. Finally, we study a local search variant of Consensus Clustering, showing W[1]-hardness for the parameter “radius of the Mirkin-distance neighborhood”. In the process, we also consider a local search variant of the related Cluster Editing problem, showing W[1]-hardness for the parameter “radius of the edge modification neighborhood”.

Optimal Planning with Global Numerical State Constraints

Automating the operations of infrastructure networks such as energy grids and oil pipelines requires a range of planning and optimisation technologies. However, current planners face significant challenges in responding to this need. Notably, they are unable to model and reason about the global numerical state constraints necessary to capture flows and similar physical phenomena occurring in these networks. A single discrete control action can affect the flow throughout the network in a way that may depend on the entire network topology. Determining whether preconditions, goals and invariant conditions are satisfied requires solving a system of numerical constraints after each action application. This paper extends domain-independent optimal planning to this kind of reasoning. We present extensions of the formalism, relaxed plans, and heuristics, as well as new search variants and experimental results on two problem domains.

New heuristic for harmonic means clustering

It is well known that some local search heuristics for \(K\)-clustering problems, such as \(k\)-means heuristic for minimum sum-of-squares clustering occasionally stop at a solution with a smaller number of clusters than the desired number \(K\). Such solutions are called degenerate. In this paper, we reveal that the degeneracy also exists in \(K\)-harmonic means (KHM) method, proposed as an alternative to \(K\)-means heuristic, but which is less sensitive to the initial solution. In addition, we discover two types of degenerate solutions and provide examples for both. Based on these findings, we give a simple method to remove degeneracy during the execution of the KHM heuristic; it can be used as a part of any other heuristic for KHM clustering problem. We use KHM heuristic within a recent variant of variable neighborhood search (VNS) based heuristic. Extensive computational analysis, performed on test instances usually used in the literature, shows that significant improvements are obtained if our simple degeneracy correcting method is used within both KHM and VNS. Moreover, our VNS based heuristic suggested here may be considered as a new state-of-the-art heuristic for solving KHM clustering problem.

An efficient distributed optimization and coordination protocol: Application to the emergency vehicle management

In this paper we deal with the distributed optimization of problems where the objective/constraints are related to the whole variables of the system. In this kind of problems we need to bring into play all the distributed agents of the system simultaneously to guarantee that the solution is feasible and of a good quality. We propose an efficient agents coordination protocol which avoids centralizing control and computation to one agent. It overcomes the shortcoming of tree search methods related to the agents order and accelerates the search process. The proposed protocol is applied to the distributed emergency vehicle management problem that consists in taking, in a distributed way, the decisions about the locations of a set of emergency vehicles in order to solve the dispatching and covering issues simultaneously. The protocol is compared to the Synchronous Limited Discrepancy Search method. It is also compared to other distributed and centralized methods. The obtained results showed the efficiency of the proposed protocol in finding good solutions in short times and its capacity to lessen the effect of the agents ordering on the solution quality.

The discrete Unconscious search and its application to uncapacitated facility location problem

In this paper a discrete variant of Unconscious search (US) for solving uncapacitated facility location problem (UFLP) is proposed. Unconscious search mimics the process of psychoanalytic psychotherapy in which the psychoanalyst tries to access the unconscious of a mental patient to find the root cause his/her problem, which is encapsulated in unconsciousness. Unconscious search is a multi-start metaheuristic which has three main stages, namely construction, construction review and local search. In construction phase a new solution is generated. In construction review the generated solution in construction phase is used to produce more starting points for using in the local search phase. The results of applying US to UFLP shows that this metaheuristic can determine high quality solutions in short processing time comparing to other heuristics.

Evaluation of a hybrid genetic tabu search framework on job shop scheduling benchmark problems

It has been well established that to find an optimal or near-optimal solution to job shop scheduling problems (JSSPs), which are NP-hard, one needs to harness different features of many techniques, such as genetic algorithms (GAs) and tabu search (TS). In this paper, we report usage of such a framework which exploits the diversified global search and the intensified local search capabilities of GA and TS, respectively. The system takes its input directly from the process information in contrast to having a problem-specific input format, making it versatile in dealing with different JSSP. This framework has been successfully implemented to solve industrial JSSPs. In this paper, we evaluate its suitability by applying it on a set of well-known job shop benchmark problems. The results have been variable. The system did find optimal solutions for moderately hard benchmark problems (40 out of 43 problems tested). This performance is similar to, and in some cases better than, comparable systems, which also establishes the versatility of the system. However for the harder benchmark problems it had difficulty in finding a new improved solution. We analyse the possible reasons for such a performance.

An adaptive two-layer particle swarm optimization with elitist learning strategy

This study presents an adaptive two-layer particle swarm optimization algorithm with elitist learning strategy (ATLPSO-ELS), which has better search capability than classical particle swarm optimization. In ATLPSO-ELS, we perform evolution on both the current swarm and the memory swarm, motivated by the tendency of the latter swarm to distribute around the problem’s optima. To achieve better control of exploration/exploitation searches in both current and memory swarms, we propose two adaptive division of labor modules to self-adaptively divide the swarms into exploration and exploitation sections. In addition, based on the orthogonal experimental design and stochastic perturbation techniques, an elitist learning strategy module is introduced in the proposed algorithm to enhance the search efficiency of swarms and to mitigate premature convergence. A comprehensive experimental study is conducted on a set of benchmark functions. Compared with various state-of-the-art PSOs and metaheuristic search variants, ATLPSO-ELS performs more competitively in the majority of the benchmark functions.

Convergence of a Hydraulic Solver with Pressure-Dependent Demands

This paper analyzes the convergence of a pressure-driven analysis (PDA) model of a water distribution network solver based on Todini’s global gradient algorithm. The PDA model is constructed by embedding a pressure−demand relationship in the EPANET simulator code. To avoid spurious convergence, a residual-based convergence error was used. The introduction of pressure-dependent demands is shown to result in a far poorer convergence. The study of solver convergence as a function of the smoothness of the pressure−demand curve has demonstrated that, statistically, a smooth pressure−demand relationship gives a somewhat better convergence. To improve convergence, use was made of a quadratic approximation of the Hazen–Williams head loss−flow relationship in the vicinity of zero and the correct implementation of the Darcy−Weisbach formula in the solver. To further improve convergence, an iteration step control technique called the line search was used. The analysis of solver convergence for different line search variants has shown that the line search in its usual form is not efficient enough and may result in poorer convergence. A necessary error decrease algorithm, whose use in the line search improves solver convergence, is proposed. It is shown that due to the convergence improvement methods the convergence of the PDA solver is somewhat better than that of the demand-driven analysis solver and sufficient for direct problems such as design, for example.

Phase Stability Analysis and Phase Equilibrium Calculations in Reactive and Nonreactive Systems Using Charged System Search Algorithms

Stochastic global optimization algorithms have shown promise in providing reliable and efficient solutions for phase stability and phase equilibrium problems in reactive and nonreactive systems. A special class of stochastic methods is Swarm Intelligence, in which search agents are allowed to interact with each other and with their environment and benefit from their peers in their collective pursuit for the global minimum, resulting in an intelligent behavior unknown to the individual agents. Of special interest are swarm intelligence methods with less tunable algorithm parameters, which allow for easy and user-friendly implementation. In particular, this study introduces the Charged System Search, a novel swarm intelligence method, as a global optimization tool to the Chemical Engineering literature via implementing it, for the first time, in solving phase stability and equilibrium problems. Two Charged System Search variants have been employed, namely, the Magnetic Charged System Search and the hybrid version with Particle Swarm Optimization. This hybrid method is coupled with chaotic maps to overcome the local optimum entrapment and to aid its exploration capability. Results indicate that these two variants generally outperformed the Charged System Search, especially the hybrid chaotic algorithm. Results of this study were also compared to those reported for other swarm intelligence methods applied in phase equilibrium calculations. In summary, this study introduces novel swarm intelligence methods for performing phase stability and equilibrium calculations in both reactive and nonreactive systems.

A diverse stochastic search algorithm for combination therapeutics.

Background. Design of drug combination cocktails to maximize sensitivity for individual patients presents a challenge in terms of minimizing the number of experiments to attain the desired objective. The enormous number of possible drug combinations constrains exhaustive experimentation approaches, and personal variations in genetic diseases restrict the use of prior knowledge in optimization. Results. We present a stochastic search algorithm that consisted of a parallel experimentation phase followed by a combination of focused and diversified sequential search. We evaluated our approach on seven synthetic examples; four of them were evaluated twice with different parameters, and two biological examples of bacterial and lung cancer cell inhibition response to combination drugs. The performance of our approach as compared to recently proposed adaptive reference update approach was superior for all the examples considered, achieving an average of 45% reduction in the number of experimental iterations. Conclusions. As the results illustrate, the proposed diverse stochastic search algorithm can produce optimized combinations in relatively smaller number of iterative steps. This approach can be combined with available knowledge on the genetic makeup of the patient to design optimal selection of drug cocktails.

Multimedia Based Community Question Answer by Harvesting Web Based Information

Community question answers have become extremely popular over the some past few years. It allows community members to share their thoughts by answering given question or acquire information from a set of answered questions. However, existing cQA forum support only textual answer which is not so informative for many community users. The cQA forum can be enhanced by adding suitable multimedia data. Multimedia question answer (MMQA) provides intuitive information with more appropriate image or video. This scheme include a rich set of information including classification of question and answer, query generation, multimedia data selection and presentation .This scheme automatically looking for which type of multimedia information should be added to enrich the textual information and it will automatically collect data from web to elaborate the answer. The data set of cQA forum processes a collection of question answer pair for comparing question with those in the dataset. The purpose of MMQA Method is to provide a multimedia data such as image and video for answering complex question. The multimedia search diversification method helps to collect the appropriate answer based on questions. It provides relevant answer to the user for more effectiveness.

A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.