A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems

This paper proposes a novel hybrid approach based on particle swarm optimization and local search, named PSOLS, for dynamic optimization problems. In the proposed approach, a swarm of particles with fuzzy social-only model is frequently applied to estimate the location of the peaks in the problem landscape. Upon convergence of the swarm to previously undetected positions in the search space, a local search agent (LSA) is created to exploit the respective region. Moreover, a density control mechanism is introduced to prevent too many LSAs crowding in the search space. Three adaptations to the basic approach are then proposed to manage the function evaluations in the way that are mostly allocated to the most promising areas of the search space. The first adapted algorithm, called HPSOLS, is aimed at improving PSOLS by stopping the local search in LSAs that are not contributing much to the search process. The second adapted, algorithm called CPSOLS, is a competitive algorithm which allocates extra function evaluations to the best performing LSA. The third adapted algorithm, called CHPSOLS, combines the fundamental ideas of HPSOLS and CPSOLS in a single algorithm. An extensive set of experiments is conducted on a variety of dynamic environments, generated by the moving peaks benchmark, to evaluate the performance of the proposed approach. Results are also compared with those of other state-of-the-art algorithms from the literature. The experimental results indicate the superiority of the proposed approach.     

Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach

In this paper, a novel heuristic algorithm is proposed to solve continuous non-linear optimization problems. The presented algorithm is a collective global search inspired by the swarm artificial intelligent of coordinated robots. Cooperative recognition and sensing by a swarm of mobile robots have been fundamental inspirations for development of Swarm Robotics Search & Rescue (SRSR). Swarm robotics is an approach with the aim of coordinating multi-robot systems which consist of numbers of mostly uniform simple physical robots. The ultimate aim is to emerge an eligible cooperative behavior either from interactions of autonomous robots with the environment or their mutual interactions between each other. In this algorithm, robots which represent initial solutions in SRSR terminology have a sense of environment to detect victim in a search & rescue mission at a disaster site. In fact, victim’s location refers to global best solution in SRSR algorithm. The individual with the highest rank in the swarm is called master and remaining robots will play role of slaves. However, this leadership and master position can be transitioned from one robot to another one during mission. Having the supervision of master robot accompanied with abilities of slave robots for sensing the environment, this collaborative search assists the swarm to rapidly find the location of victim and subsequently a successful mission. In order to validate effectiveness and optimality of proposed algorithm, it has been applied on several standard benchmark functions and a practical electric power system problem in several real size cases. Finally, simulation results have been compared with those of some well-known algorithms. Comparison of results demonstrates superiority of presented algorithm in terms of quality solutions and convergence speed.     

Snap-drift cuckoo search: A novel cuckoo search optimization algorithm

Cuckoo search (CS) is one of the well-known evolutionary techniques in global optimization. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploration and exploitation. To address these issues, a new CS extension namely snap-drift cuckoo search (SDCS) is proposed in this study. The proposed algorithm first employs a learning strategy and then considers improved search operators. The learning strategy provides an online trade-off between local and global search via two snap and drift modes. In snap mode, SDCS tends to increase global search to prevent algorithm of being trapped in a local minima; and in drift mode, it reinforces the local search to enhance the convergence rate. Thereafter, SDCS improves search capability by employing new crossover and mutation search operators. The accuracy and performance of the proposed approach are evaluated by well-known benchmark functions. Statistical comparisons of experimental results show that SDCS is superior to CS, modified CS (MCS), and state-of-the-art optimization algorithms in terms of convergence speed and robustness.     

The Witsenhausen counterexample: a hierarchical search approach fornonconvex optimization problems

The Witsenhausens counterexample (1968) is a difficult nonconvex functional optimization problem which has been outstanding for more than 30 years. Considerable amount of literature has been accumulated, but optimal solutions remain elusive. In this paper, we develop a framework that allows one to gain additional new insights to the properties of a better solution for a benchmark instance. Through our approach, we are able to zero in on a solution that is 13% better than the previously known best solution, and more than 54% better than previous results obtained by other authors. More importantly, we demonstrate that our approach, called hierarchical search, can be useful in general optimization problems     

An efficient frontier approach to addressing JIT sequencing problems with setups via search heuristics

This research presents a technique to obtain production sequences where scheduling flexibility and number of setups are considered. These two objectives of flexibility and setups are typically inversely correlated with each other, so simultaneous optimization of both is challenging. Another challenging issue is the combinatorial nature of such sequencing problems. An efficient frontier approach is exploited where simultaneous maximization of flexibility and minimization of setups is desired. The efficient frontier is constructed via the popular search heuristics of Genetic Algorithms, Simulated Annealing and Tabu Search. For several test problems from the literature, it is discovered that the efficient frontiers obtained via the three search heuristics provide near optimal results for smaller problems, and for larger problems, the Simulated Annealing and Tabu Search approaches outperform the Genetic Algorithm approach.     

A novel group search optimizer for multi-objective optimization

In this paper, a novel multi-objective group search optimizer named NMGSO is proposed for solving the multi-objective optimization problems. To simplify the computation, the scanning strategy of the original GSO is replaced by the limited pattern search procedure. To enrich the search behavior of the rangers, a special mutation with a controlling probability is designed to balance the exploration and exploitation at different searching stages and randomness is introduced in determining the coefficients of members to enhance the diversity. To handle multiple objectives, the non-dominated sorting scheme and multiple producers are used in the algorithm. In addition, the kernel density estimator is used to keep diversity. Simulation results based on a set of benchmark functions and comparisons with some methods demonstrate the effectiveness and robustness of the proposed algorithm, especially for the high-dimensional problems.     

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.     

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.     

A harmony search algorithm for high-dimensional multimodal optimization problems

Harmony search (HS) and its variants have been found successful applications, however with poor solution accuracy and convergence performance for high-dimensional (≥200) multimodal optimization problems. The reason is mainly huge search space and multiple local minima. To tackle the problem, we present a new HS algorithm called DIHS, which is based on Dynamic-Dimensionality-Reduction-Adjustment (DDRA) and dynamic fret width (fw) strategy. The former is for avoiding generating invalid solutions and the latter is to balance global exploration and local exploitation. Theoretical analysis on the DDRA strategy for success rate of update operation is given and influence of related parameters on solution accuracy is investigated. Our experiments include comparison on solution accuracy and CPU time with seven typical HS algorithms and four widely used evolutionary algorithms (SaDE, CoDE, CMAES and CLPSO) and statistical comparison by the Wilcoxon Signed-Rank Test with the seven HS algorithms and four evolutionary algorithms. The problems in experiments include twelve multimodal and four complex uni-modal functions with high-dimensionality.Experimental results indicate that the proposed approach can provide significant improvement on solution accuracy with less CPU time in solving high-dimensional multimodal optimization problems, and the more dimensionality that the optimization problem is, the more benefits it provides.     

Structural analysis of local search heuristics in combinatorial optimization

Neighborhood, or local, search is a popular and practical heuristic for many combinational optimization problems. We examine the neighborhood structures of two classes of problems, 0–1 integer programming and the mean tardiness job sequencing problem—from the viewpoint of state-space graphs in artificial intelligence. Such analysis is shown to provide fundamental insights into the nature of local search algorithms and provides a useful framework for evaluating and comparing such heuristics. Computational results are presented to support these observations.     

Local search heuristics for two-stage flow shop problems with secondary criterion

This paper develops and compares different local search heuristics for the two-stage flow shop problem with makespan minimization as the primary criterion and the minimization of either the total flow time, total weighted flow time, or total weighted tardiness as the secondary criterion. We investigate several variants of simulated annealing, threshold accepting, tabu search, and multi-level search algorithms. The influence of the parameters of these heuristics and the starting solution are empirically analyzed. The proposed heuristic algorithms are empirically evaluated and found to be relatively more effective in finding better quality solutions than the existing algorithms.Scope and purposeTraditional research to solve multi-stage scheduling problems has focused on single criterion. However, in industrial scheduling practices, managers develop schedules based on multi-criteria. Scheduling problems involving multiple criteria require significantly more effort in finding acceptable solutions and hence have not received much attention in the literature. This paper considers one such multiple criteria scheduling problem, namely, the two-machine flow shop problem where the primary criterion is the minimization of makespan and the secondary criterion is one of the three most popular performance measures, namely, the total flow time, total weighted flow time, or total weighted tardiness. Based on the principles of local search, development of heuristic algorithms, that can be adapted for several multi-criteria scheduling problems, is discussed. Using the example of the two-machine flow shop problem with secondary criterion, computational experiments are used to evaluate the utility of the proposed algorithms for solving scheduling problems with a secondary criterion.     

Runtime analysis of search heuristics on software engineering problems

Many software engineering tasks can potentially be automated using search heuristics. However, much work is needed in designing and evaluating search heuristics before this approach can be routinely applied to a software engineering problem. Experimental methodology should be complemented with theoretical analysis to achieve this goal. Recently, there have been significant theoretical advances in the runtime analysis of evolutionary algorithms (EAs) and other search heuristics in other problem domains. We suggest that these methods could be transferred and adapted to gain insight into the behaviour of search heuristics on software engineering problems while automating software engineering.     

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.

     

A parallel boundary search particle swarm optimization algorithm for constrained optimization problems

During the past decade, considerable research has been conducted on constrained optimization problems (COPs) which are frequently encountered in practical engineering applications. By introducing resource limitations as constraints, the optimal solutions in COPs are generally located on boundaries of feasible design space, which leads to search difficulties when applying conventional optimization algorithms, especially for complex constraint problems. Even though penalty function method has been frequently used for handling the constraints, the adjustment of control parameters is often complicated and involves a trial-and-error approach. To overcome these difficulties, a modified particle swarm optimization (PSO) algorithm named parallel boundary search particle swarm optimization (PBSPSO) algorithm is proposed in this paper. Modified constrained PSO algorithm is adopted to conduct global search in one branch while Subset Constrained Boundary Narrower (SCBN) function and sequential quadratic programming (SQP) are applied to perform local boundary search in another branch. A cooperative mechanism of the two branches has been built in which locations of the particles near boundaries of constraints are selected as initial positions of local boundary search and the solutions of local boundary search will lead the global search direction to boundaries of active constraints. The cooperation behavior of the two branches effectively reinforces the optimization capability of the PSO algorithm. The optimization performance of PBSPSO algorithm is illustrated through 13 CEC06 test functions and 5 common engineering problems. The results are compared with other state-of-the-art algorithms and it is shown that the proposed algorithm possesses a competitive global search capability and is effective for constrained optimization problems in engineering applications.     

一种新的基于强化学习改进SAR的无人机路径规划

搜索和救援优化算法(SAR)是2020年提出的模拟搜救行为的一种元启发式优化算法,用来解决工程中的约束优化问题.但是, SAR存在收敛慢、个体不能自适应选择操作等问题,鉴于此,提出一种新的基于强化学习改进的SAR算法(即RLSAR).该算法重新设计SAR的局部搜索和全局搜索操作,并增加路径调整操作,采用异步优势演员评论家算法(A3C)训练强化学习模型使得SAR个体获得自适应选择算子的能力.所有智能体在威胁区数量、位置和大小均随机生成的动态环境中训练,进而从每个动作的贡献、不同威胁区下规划出的路径长度和每个个体的执行操作序列3个方面对训练好的模型进行探索性实验.实验结果表明, RLSAR比标准SAR、差分进化算法、松鼠搜索算法具有更高的收敛速度,能够在随机生成的三维动态环境中成功地为无人机规划出更加经济且安全有效的可行路径,表明所提出算法可作为一种有效的无人机路径规划方法.     

A novel global harmony search algorithm for reliability problems

Inspired by the swarm intelligence of particle swarm, a novel global harmony search algorithm (NGHS) is proposed to solve reliability problems in this paper. The proposed algorithm includes two important operations: position updating and genetic mutation with a small probability. The former enables the worst harmony of harmony memory to move to the global best harmony rapidly in each iteration, and the latter can effectively prevent the NGHS from trapping into the local optimum. Based on a large number of experiments, the proposed algorithm has demonstrated stronger capacity of space exploration than most other approaches on solving reliability problems. The results show that the NGHS can be an efficient alternative for solving reliability problems.     

A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems

In this study, we propose a novel quantum-inspired evolutionary algorithm (QEA), called quantum inspired Tabu search (QTS). QTS is based on the classical Tabu search and characteristics of quantum computation, such as superposition. The process of qubit measurement is a probability operation that increases diversification; a quantum rotation gate used to searching toward attractive regions will increase intensification. This paper will show how to implement QTS into NP-complete problems such as 0/1 knapsack problems, multiple knapsack problems and the traveling salesman problem. These problems are important to computer science, cryptography and network security. Furthermore, our experimental results on 0/1 knapsack problems are compared with those of other heuristic algorithms, such as a conventional genetic algorithm, a Tabu search algorithm and the original QEA. The final outcomes show that QTS performs much better than other heuristic algorithms without premature convergence and with more efficiency. Also on multiple knapsack problems and the traveling salesman problem QTS verify its effectiveness.     

A novel approach to discrete truss design problems using mixed integer neighborhood search

A new approach for the multi-objective optimization of composite structures under the effects of uncertainty in mechanical properties, structural parameters and external loads is proposed, to guarantee higher levels of accuracy exclusively with Evolutionary Algorithms (EA). The concepts of Reliability-Based Robust Design Optimization (RBRDO) are applied. Optimality is defined as the minimization of the structural weight and robustness as the minimization of the determinant of the variance-covariance matrix of the structural responses. Reliability assessment is performed through a mathematical reformulation of the Performance Measure Approach, suitable for EA, where the standard normal uncertainty space was defined in directional coordinates and reduced to the surface of the hypersphere of radius β^^a. A binary reliability constraint, that allowed avoiding unnecessary runs of the reliability inner-cycle is defined. The Robust Design Optimization cycle is solved by a multi-objective EA, based on constrained-dominance. Sensitivities of the structural responses, necessary for uncertainty analysis only, are calculated analytically by the Adjoint Variable Method. A numerical example considering a balanced angle-ply laminate shell is presented. Results show an effective convergence of the Pareto-optimal Front (POF). Uncertainty analysis shows that the variability of the critical displacements increases along the POF. For the stresses, variability is stable but of higher values. The incorporation of the reliability constraint prevents the natural decrease of the reliability index, along the POF, to reach levels too close, or inside, of the failure domain. The distribution of the reliability measures along the POF is similar and demonstrates the effects of reliability in the RBRDO procedure.     

A dynamic optimization approach for adaptive incremental learning

A fundamental problem when performing incremental learning is that the best set of a classification system's parameters can change with the evolution of the data. Consequently, unless the system self‐adapts to such changes, it will become obsolete, even if the application environment seems to be static. To address this problem, we propose a dynamic optimization approach in this paper that performs incremental learning in an adaptive fashion by tracking, evolving, and combining optimum hypotheses overtime. The approach incorporates various theories, such as dynamic particle swarm optimization, incremental support vector machine classifiers, change detection, and dynamic ensemble selection based on classifiers' confidence levels. Experiments carried out on synthetic and real‐world databases demonstrate that the proposed approach actually outperforms the classification methods often used in incremental learning scenarios. © 2011 Wiley Periodicals, Inc.     

Improved cuckoo search for reliability optimization problems

An efficient approach to solve engineering optimization problems is the cuckoo search algorithm. It is a recently developed meta-heuristic optimization algorithm. Normally, the parameters of the cuckoo search are kept constant. This may result in decreasing the efficiency of the algorithm. To cope with this issue, the cuckoo search parameters should be tuned properly. In this paper, an improved cuckoo search algorithm, enhancing the accuracy and convergence rate of the cuckoo search algorithm, is presented. Then, the performance of the proposed algorithm is tested on some complex engineering optimization problems. They are four well-known reliability optimization problems, a large-scale reliability optimization problem as well as a complex system, which is a 15-unit system reliability optimization problem. Finally, the results are compared with those given by several well-known methods. Simulation results demonstrate the effectiveness of the proposed algorithm.