Three improved hybrid metaheuristic algorithms for engineering design optimization

This paper presents three hybrid metaheuristic algorithms that further improve the two hybrid differential evolution (DE) metaheuristic algorithms described in Liao [1]. The three improved algorithms are: (i) MDE′–HJ, which is a modification of MA–MDE′ in Liao [1] by replacing the random walk with direction exploitation local search with the Hooke and Jeeves (HJ) method; (ii) MDE′–IHS–HJ, which is constructed by adding the Hooke and Jeeves method to the original cooperative hybrid, i.e., MDE′–IHS; and (iii) PSO–MDE′–HJ, which is a variation of MDE′–IHS–HJ by replacing improved harmony search (IHS) with particle search optimization (PSO). A comprehensive comparative study was carried out to compare the three improved hybrids with the three algorithms presented by Liao [1] in terms of average success rate, average function evaluations taken, average elapsed CPU time, and convergence profiles. A total of 18 problems, 4 more than those used in Liao [1], were selected from different engineering domains for testing. The test results indicate that all three new hybrids can achieve higher success rate in much less CPU time. Among these three hybrids, MDE′–IHS–HJ is the best one in terms of success rate, better than the best hybrid in Liao [1] by over 15% and better than the second best, PSO–MDE′–HJ, by nearly 10%.     

A metaheuristic approach for optical network optimization problems

Two of the most complex optimization problems encountered in the design of third generation optical networks are the dynamic routing and wavelength assignment (DRWA) problem under the assumptions of ideal and non-ideal physical layers. Both these problems are NP-complete in nature. These are challenging due to the presence of multiple local optima in the search space. Even heuristics-based algorithms fail to solve these problems efficiently as the search space is non-convex. This paper reports the performance of a metaheuristic, that is, an evolutionary programming algorithm in solving different optical network optimization problems. The primary motivation behind adopting this approach is to reduce the algorithm execution time. It is demonstrated that the same basic approach can be used to solve different optimization problems by designing problem-specific fitness functions. Also, it is shown how the algorithm performance can be improved by integrating suitable soft constraints with the original constraints. Exhaustive simulation studies are carried out assuming the presence of different levels of linear impairments such as switch and demultiplexer crosstalk and non-linear impairments like four wave mixing to illustrate the superiority of the proposed algorithms.     

Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems

Optimization techniques, specially evolutionary algorithms, have been widely used for solving various scientific and engineering optimization problems because of their flexibility and simplicity. In this paper, a novel metaheuristic optimization method, namely human behavior-based optimization (HBBO), is presented. Despite many of the optimization algorithms that use nature as the principal source of inspiration, HBBO uses the human behavior as the main source of inspiration. In this paper, first some human behaviors that are needed to understand the algorithm are discussed and after that it is shown that how it can be used for solving the practical optimization problems. HBBO is capable of solving many types of optimization problems such as high-dimensional multimodal functions, which have multiple local minima, and unimodal functions. In order to demonstrate the performance of HBBO, the proposed algorithm has been tested on a set of well-known benchmark functions and compared with other optimization algorithms. The results have been shown that this algorithm outperforms other optimization algorithms in terms of algorithm reliability, result accuracy and convergence speed.

     

Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Lévy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.     

PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

A conventional collaborative beamforming (CB) system suffers from high sidelobes due to the random positioning of the nodes. This paper introduces a hybrid metaheuristic optimization algorithm called the Particle Swarm Optimization and Gravitational Search Algorithm-Explore (PSOGSA-E) to suppress the peak sidelobe level (PSL) in CB, by the means of finding the best weight for each node. The proposed algorithm combines the local search ability of the gravitational search algorithm (GSA) with the social thinking skills of the legacy particle swarm optimization (PSO) and allows exploration to avoid premature convergence. The proposed algorithm also simplifies the cost of variable parameter tuning compared to the legacy optimization algorithms. Simulations show that the proposed PSOGSA-E outperforms the conventional, the legacy PSO, GSA and PSOGSA optimized collaborative beamformer by obtaining better results faster, producing up to 100% improvement in PSL reduction when the disk size is small.     

CDEPSO: a bi-population hybrid approach for dynamic optimization problems

Many real-world optimization problems are dynamic, in which the environment, i.e. the objective function and restrictions, can change over time. In this case, the optimal solution(s) to the problem may change as well. These problems require optimization algorithms to continuously and accurately track the trajectory of the optima (optimum) through the search space. In this paper, we propose a bi-population hybrid collaborative model of Crowding-based Differential Evolution (CDE) and Particle Swarm Optimization (PSO) for Dynamic Optimization Problems (DOPs). In our approach, called CDEPSO, a population of genomes is responsible for locating several promising areas of the search space and keeping diversity throughout the run using CDE. Another population is used to exploit the area around the best found position using the PSO. Several mechanisms are used to increase the efficiency of CDEPSO when finding and tracking peaks in the solution space. A set of experiments was carried out to evaluate the performance of the proposed algorithm on dynamic test instances generated using the Moving Peaks Benchmark (MPB). Experimental results show that the proposed approach is effective in dealing with DOPs.     

HMOSHSSA: a hybrid meta-heuristic approach for solving constrained optimization problems

This paper proposes a novel hybrid multi-objective optimization algorithm named HMOSHSSA by synthesizing the strengths of Multi-objective Spotted Hyena Optimizer (MOSHO) and Salp Swarm Algorithm (SSA). HMOSHSSA utilizes the exploration capability of MOSHO to explore the search space effectively and leader and follower selection mechanism of SSA to achieve global best solution with faster convergence. The proposed algorithm is evaluated on 24 benchmark test functions, and its performance is compared with seven well-known multi-objective optimization algorithms. The experimental results demonstrate that HMOSHSSA acquires very competitive results and outperforms other algorithms in terms of convergence speed, search-ability and accuracy. Additionally, HMOSHSSA is also applied on seven well-known engineering problems to further verify its efficacy. The results reveal the effectiveness of proposed algorithm toward solving real-life multi-objective optimization problems.

     

A new hybrid approach for dynamic continuous optimization problems

A new hybrid approach for dynamic optimization problems with continuous search spaces is presented. The proposed approach hybridizes efficient features of the particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. In the proposed dynamic hybrid PSO (DHPSO) algorithm, the swarm size is varied in a self-regulatory manner. Inspired from the microbial life, the particles can reproduce infants and the old ones die. The infants are especially reproduced by high potential particles and located near the local optimum points, using the quadratic interpolation method. The algorithm is adapted to perform in continuous search spaces, utilizing continuous movement of the particles and using Euclidian norm to define the neighborhood in the reproduction procedure. The performance of the new proposed approach is tested against various benchmark problems and compared with those of some other heuristic optimization algorithms. In this regard, different types of dynamic environments including periodic, linear and random changes are taken with different performance metrics such as real-time error, offline performance and offline error. The results indicate a desirable better efficiency of the new algorithm over the existing ones.     

An approach to multicriterion optimization problems for engineering design

Optimization problems in mechanical engineering design are often modelled as nonlinear programming problems. A multicriterion optimization approach to this problem is developed in this work. The problem formulation is given, and the min-max principle for this problem is discussed. Next, an algorithm is provided for comparing solutions using this principle.The solution which is defined by the min-max principle of optimality may be called the best compromise considering all the criteria simultaneously and on equal terms of importance. This principle is fully formalized mathematically and used to obtain the optimal solution automatically. The algorithm for comparing solutions gives us, from any set of solutions, the one which is optimal in the min-max sense.Seeking the optimal solution in the min-max sense can be carried out in many different ways. Some methods based upon the Monte Carlo method and trade-off studies are proposed.The approach as discussed here is applied to the design of machine tool gearboxes. The problem is formulated as finding the basic constructional parameters (modules, numbers of teeth etc.) of a gearbox which minimizes simultaneously four objective functions: volume of elements, peripheral velocity between gears, width of gearbox and distance between axes of input and output shafts. A detailed example considering a lathe gearbox optimization problem is also presented. This example indicates that for some mechanical engineering optimization problems, using this approach, we can automatically obtain a solution which is optimal and acceptable to the designer.     

Nonlinear continuous multi-response problems: a novel two-phase hybrid genetic based metaheuristic

Generally the most real world production systems are tackling several different responses and the problem is optimizing these responses concurrently. This study strives to present a new two-phase hybrid genetic based metaheuristic for optimizing nonlinear continuous multi-response problems. Premature convergence and getting stuck in local optima, which makes the algorithm time consuming, are common problems dealing with genetic algorithms (GAs). So we hybridize GA with a clustering approach and particle swarm optimization algorithm (PSO) to make a balanced relationship between time consuming and premature termination. The proposed algorithm also tries to find Ideal Points (IPs) for response functions. IPs are considered as improvement measures that determine when PSO should start. PSO based local search exploit Pareto archive solutions to enhance performance of the algorithm by expanding the search space. Since there is no standard benchmark in this field, we use two case studies from distinguished paper in multi-response optimization and compare the results with some of the mentioned algorithms in the literature. Results show the outperformance of the proposed algorithm than all of them.     

Cultural algorithms: a Tabu search approach for the optimization of engineering design problems

Cultural Algorithms and Tabu search algorithms are both powerful tools to solve intricate constrained engineering and large-scale multi-modal optimization problems. In this paper, we introduce a hybrid approach that combines Cultural Algorithms and Tabu search (CA–TS). Here, Tabu Search is used to transform History Knowledge in the Belief Space from a passive knowledge source to an active one. In each generation of the Cultural Algorithm, we calculate the best individual solution and then seek the best new neighbor of that solution in the social network for that population using Tabu search. In order to speed up the convergence process through knowledge dissemination, simple forms of social network topologies were used to describe the connectivity of individual solutions. This can reduce the number of needed generations while maintaining accuracy and increasing the search radius when needed. The integration of the Tabu search algorithm as a local enhancement process enables CA–TS to leap over false peaks and local optima. The proposed hybrid algorithm is applied to a set of complex non-linear constrained engineering optimization design problems. Furthermore, computational results are discussed to show that the algorithm can produce results that are comparable or superior to those of other well-known optimization algorithms from the literature, and can improve the performance and the speed of convergence with a reduced communication cost.     

A hybrid metaheuristic approach for the capacitated p-median problem

The capacitated p-median problem (CPMP) seeks to obtain the optimal location of p medians considering distances and capacities for the services to be given by each median. This paper presents an efficient hybrid metaheuristic algorithm by combining a proposed cutting-plane neighborhood structure and a tabu search metaheuristic for the CPMP. In the proposed neighborhood structure to move from the current solution to a neighbor solution, an open median is selected and closed. Then, a linear programming (LP) model is generated by relaxing binary constraints and adding new constraints. The generated LP solution is improved using cutting-plane inequalities. The solution of this strong LP is considered as a new neighbor solution. In order to select an open median to be closed, several strategies are proposed. The neighborhood structure is combined with a tabu search algorithm in the proposed approach. The parameters of the proposed hybrid algorithm are tuned using design of experiments approach. The proposed algorithm is tested on several sets of benchmark instances. The statistical analysis shows efficiency and effectiveness of the hybrid algorithm in comparison with the best approach found in the literature.     

Applications and analysis of bio-inspired eagle strategy for engineering optimization

All swarm-intelligence-based optimization algorithms use some stochastic components to increase the diversity of solutions during the search process. Such randomization is often represented in terms of random walks. However, it is not yet clear why some randomization techniques (and thus why some algorithms) may perform better than others for a given set of problems. In this work, we analyze these randomization methods in the context of nature-inspired algorithms. We also use eagle strategy to provide basic observations and relate step sizes and search efficiency using Markov theory. Then, we apply our analysis and observations to solve four design benchmarks, including the designs of a pressure vessel, a speed reducer, a PID controller, and a heat exchanger. Our results demonstrate that eagle strategy with Lévy flights can perform extremely well in reducing the overall computational efforts.     

An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems

Scheduling for the flexible job-shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. The combining of several optimization criteria induces additional complexity and new problems. Particle swarm optimization is an evolutionary computation technique mimicking the behavior of flying birds and their means of information exchange. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations, including scheduling and sequencing. By reasonably hybridizing these two methodologies, we develop an easily implemented hybrid approach for the multi-objective flexible job-shop scheduling problem (FJSP). The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the multi-objective FJSP, especially for problems on a large scale.     

Parallel machine total tardiness scheduling with a new hybrid metaheuristic approach

This work proposes a hybrid metaheuristic (HMH) approach which integrates several features from tabu search (TS), simulated annealing (SA) and variable neighbourhood search (VNS) in a new configurable scheduling algorithm. In particular, either a deterministic or a random candidate list strategy can be used to generate the neighbourhood of a solution, both a tabu list mechanism and the SA probabilistic rule can be adopted to accept solutions, and the dimension of the explored neighbourhood can be dynamically modified. The considered class of scheduling problems is characterized by a set of independent jobs to be executed on a set of parallel machines with non-zero ready times and sequence dependent setups. In particular, the NP-hard generalized parallel machine total tardiness problem (GPMTP) recently defined by Bilge et al. [A tabu search algorithm for parallel machine total tardiness problem. Computers & Operations Research 2004;31:397–414], is faced. Several alternative configurations of the HMH have been tested on the same benchmark set used by Bilge et al. The results obtained highlight the appropriateness of the proposed approach.     

Toward a new wavelet-based watermarking approach for color images using bio-inspired optimization principles

This paper presents a novel watermarking approach for copyright protection of color images based on the wavelet transformation. We consider the problem of logo watermarking and employ the genetic algorithm optimization principles to obtain performance improvement with respect to the existing algorithms. In the proposed method, the strength of the embedded watermark is controlled locally and according to the visual properties of the host signal. These parameters are varied to find the most suitable ones for images with different characteristics. The experimental results show that the proposed algorithm yields a watermark which is invisible to human eyes and robust to a wide variety of common attacks.     

Ride sharing with flexible participants: a metaheuristic approach for large‐scale problems

Severe traffic congestion and growing ecological consciousness have led to the rise of alternative transportation systems. Ride sharing is one such alternative in which drivers and passengers with similar time schedules and travel plans are matched. For this service to be effective, a large number of users are required to increase the probability of finding suitable travel partners. The present paper proposes a late acceptance metaheuristic to decide which users act as drivers and to construct their routes. The underlying optimization model allows passengers to walk to/from alternative pickup/drop‐off locations so as to further exploit user flexibility. A computational study quantifies the impact of different types of participant flexibility on CO₂ emissions. These insights can inform and support policymakers in organizing effective ride‐sharing systems.     

HColonies: a new hybrid metaheuristic for medical data classification

Medical data feature a number of characteristics that make their classification a complex task. Yet, the societal significance of the subject and the computational challenge it presents has caused the classification of medical datasets to be a popular research area. A new hybrid metaheuristic is presented for the classification task of medical datasets. The hybrid ant–bee colonies (HColonies) consists of two phases: an ant colony optimization (ACO) phase and an artificial bee colony (ABC) phase. The food sources of ABC are initialized into decision lists, constructed during the ACO phase using different subsets of the training data. The task of the ABC is to optimize the obtained decision lists. New variants of the ABC operators are proposed to suit the classification task. Results on a number of benchmark, real-world medical datasets show the usefulness of the proposed approach. Classification models obtained feature good predictive accuracy and relatively small model size.     

Simulation-based optimization vs. mathematical programming: A hybrid approach for optimizing scheduling problems

With the increasing computing power of modern processors, exact solution methods (solvers) for the optimization of scheduling problems become more and more important. Based on the mixed integer programming (MIP) formulation of a scheduling problem, it will be analyzed how powerful the present solvers of this problem class are and up to which complexity real scheduling problems are manageable. For this, initially some common benchmark problems are investigated to find out the boundaries for practical application. Then, the acquired results will be compared with the results of a conventional simulation-based optimization approach under comparable time restrictions. As a next step, the general advantages and disadvantages of both approaches were analyzed. As the result, a coupling of the discrete event simulation system and an MIP solver is presented. This coupling automatically generates an MIP-formulation for the present simulation model which can be solved externally by an MIP solver. After the external optimization process follows a backward transformation of the results into the simulation system. All features of the simulation system (like Gantt-Charts, etc.) could be used to check or to illustrate these results. To perform the coupling for a wide range of simulation models, it has to be defined which general constraints the model has to satisfy.