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Abstract: In this paper, we investigate the optimal locations of surveillance cameras on straight lanes. As known, inappropriate settings of surveillance cameras will result in some dead angles and dead space, and oversetting of surveillance cameras will waste limited resources. The considered location problem aims to minimize the maximal detection failure probability of points on straight lane subject to a limited budget. The problem is an important and complex location problem in practice. The purpose of this paper is twofold. Firstly, exact closed forms of optimal locations of single-type surveillance cameras are derived. Secondly, we apply a PSO algorithm for solving the types and locations of multiple-type surveillance cameras. Numerical results are reported and compared with those by well known software LINGO. Limited numerical results show the effectiveness of PSO especially when the budget is limited. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new fault prediction model is presented to deal with the fault prediction problems in the presence of both quantitative and qualitative data based on the dynamic evidential reasoning (DER) approach. In engineering practice, system performance is constantly changed with time. As such, there is a need to develop a supporting mechanism that can be used to conduct dynamic fusion with time, and establish a prediction model to trace and predict system performance. In this paper, a DER approach is first developed to realize dynamic fusion. The new approach takes account of time effect by introducing belief decaying factor, which reflects the nature that evidence credibility is decreasing over time. Theoretically, it is show that the new DER aggregation schemes also satisfy the synthesis theorems. Then a fault prediction model based on the DER approach is established and several optimization models are developed for locally training the DER prediction model. The main feature of these optimization models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune the DER prediction model whose initial parameters are decided by expert’s knowledge or common sense. Finally, two numerical examples are provided to illustrate the detailed implementation procedures of the proposed approach and demonstrate its potential applications in fault prediction. [ABSTRACT FROM AUTHOR]

This study considers a constrained huge-scale optimization problem over networks where the objective is to minimize the sum of nonsmooth local loss functions. To solve this problem, many optimization algorithms have been proposed by employing (sub)gradient descent methods to handle high-dimensional data, but the computation of the entire sub(gradient) becomes a computational bottleneck. To reduce the computational burden of each agent and preserve privacy of data in time-varying networks, we propose a privacy-preserving decentralized randomized block-coordinate subgradient projection algorithm over time-varying networks, in which the coordinates of the subgradient vector is randomly chosen to update the optimized parameter and the partially homomorphic cryptography is used to protect the privacy of data. Further, we prove that our algorithm is convergent asymptotically. Moreover, the rates of convergence are also established by choosing appropriate step sizes, i.e., O (log K / K) under local strong convexity and O (log K / K) under local convexity, in which K represents the number of iterations. Meanwhile, we show that the privacy of data can be protected by the proposed algorithm. The results of experiments demonstrate the computational benefit of our algorithm on two real-world datasets. The theoretical results are also verified by different experiments. • This paper proposes a privacy-preserving decentralized randomized block-coordinate subgradient projection algorithm. • It proves that the proposed algorithm is asymptotically convergent. • It proves that the proposed algorithm can protect the privacy of data. • It proves that the rate of convergence is achieved, i.e., O (log K / K) and O (log K / K). [ABSTRACT FROM AUTHOR]

The component pick-and-place sequence is one of the key factors to affect the working efficiency of the surface mounting machine in the printed circuit board assembly. In this paper, an improved Shuffled Frog-leaping Algorithm was presented by improving the basic Shuffled Frog-leaping Algorithm (SFLA) with the strategy of letting all frogs taking part in memetic evolution and adding the self-variation behavior to the frog. The objective function of component pick-and-place sequence of the gantry multi-head component surface mounting machine was established. Parameters selection is critical for SFLA. In this study, Three-way ANOVA was used in parameters analyzing of the new improved SFLA. The parameters like memeplex numbers m, the frogs' number P and local evolution numbers iPart were found having notable effects on the mounting time (time spent for components picking and placing), but the interactions among these parameters were not obvious. Multiple comparison procedures were adopted to determine the best parameter settings. In order to test the performance of the new algorithm, several experiments were carried out to compare the performance of improved SFLA with the basic SFLA and the genetic algorithm (GA) in solving the component pick-and-place sequence optimization problems. The experiment results indicate that improved SFLA can solve the optimization problem efficiently and outperforms SFLA and GA in terms of convergence accuracy, although more CPU time is undeniably needed. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. [ABSTRACT FROM AUTHOR]

Students generally attempt to improve themselves with different manners in their spare time. Inspired by this fact, this paper presents a multi-objective elitist feedback teaching–learning-based optimization (MEFTO) algorithm for multi-objective optimization problems. To promote the exploration capacity and convergence of the basic teaching–learning-based optimization (TLBO), a feedback phase that simulates the spare-time learning phenomenon is introduced at the end of the learner phase. Students should compare their score with the average class score to select an appropriate means for further improvement. Poorly performing students can learn from the teacher directly for rapid improvement, whereas high-performing students prefer to motivate themselves for reinforcement learning. Non-dominated sorting is incorporated to permit this heuristic to solve problems with several objectives. Crowding distance calculation is adopted to maintain the diversity of the obtained solution set in a single run. Elitism strategy is also employed to provide a great improvement for the algorithm. The performance of the MEFTO algorithm is compared with seven other well-known algorithms by using a set of unconstrained benchmark test problems and three constrained engineering optimization problems. The qualitative and quantitative results indicate that the proposed algorithm can provide considerably competitive results and outperforms the other algorithms. • A novel multi-objective evolutionary algorithm (MEFTO) is developed. • The developed algorithm is evaluated on a series of benchmark functions. • The performance of the MEFTO is demonstrated using various metrics. • The developed algorithm is applied to solve three constrained engineering problem. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an optimization-based model for generic document summarization. The model generates a summary by extracting salient sentences from documents. This approach uses the sentence-to-document collection, the summary-to-document collection and the sentence-to-sentence relations to select salient sentences from given document collection and reduce redundancy in the summary. To solve the optimization problem has been created an improved differential evolution algorithm. The algorithm can adjust crossover rate adaptively according to the fitness of individuals. We implemented the proposed model on multi-document summarization task. Experiments have been performed on DUC2002 and DUC2004 data sets. The experimental results provide strong evidence that the proposed optimization-based approach is a viable method for document summarization. [Copyright &y& Elsevier]

Abstract: Enhanced index tracking is a popular strategy in portfolio management that focuses on adding reliable value relative to the index on the basis of mimicking the behavior of the benchmark index. In this paper, we propose a multi-objective optimization scheme for the enhanced index tracking problem, which provides the framework of defining the objectives as both maximizing the degree of beating the benchmark index and minimizing the accumulated error of underperforming the benchmark. Transaction costs are limited in the constraints. An immunity-based multi-objective optimization algorithm is presented to search for the solution of the enhanced index tracking problem. Treatment of infeasibility and solution selection are also presented. Our proposed approach is implemented to five data sets drawn from major world markets. The computational results compared with other published results show that our method has superior performance. [ABSTRACT FROM AUTHOR]

Abstract: The permutation flow shop scheduling problem is addressed in this paper. Two objectives, minimization of makespan and total flow time, are considered. We propose a memetic algorithm, called NNMA, by integrating a general multiobjective evolutionary algorithm (NSGA-II) with a problem-specific heuristic (NEH). We take NEH as a local improving procedure in NNMA and propose several adaptations including the acceptance criterion and job-insertion ordering to deal with multiple objectives and to improve its performance. We test the performance of NNMA using 90 public problem instances with different problem scales, and compare its performance with 23 algorithms. The experimental results show that our NNMA provides close performance for 30 small-scale instances and better performance for 50 medium- and large-scale instances. Furthermore, more than 70% of the net set of non-dominated solutions is updated by NNMA for these 50 instances. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes new Differential Evolution (DE) algorithm, named as Chaotically Initialized Differential Evolution (CIDE), that uses chaotic maps with certainty, ergodicity, unpredictability, non-periodicity, complex temporal behavior and the stochastic property for initial population generation. This has been done by using of different chaotic number generators each time a random number is needed by the classical DE algorithm in its initial step. Seven chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of DE and complex dynamics, can improve the quality of results in some optimization problems and also that chaos may be a desired process. It has been also shown that, Sinus map and Circle map have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions. [Copyright &y& Elsevier]

This paper introduces the algorithm portfolio concept to solve a combinatorial optimization problem pertaining to a supply chain. The supply chain problem is modeled with capacity constraints and demand variations over different time periods to minimize the total supply chain configuration cost. The algorithm portfolio is implemented over various problem instances to inspect and alleviate the computational expensiveness of a solution strategy. A bunch of five algorithms are utilized hereby viz. AIS, GA, Endosymbiotic Optimization, PSO and Psychoclonal algorithm. The observations reflect the appropriateness and effect of algorithm portfolios over the adopted supply chain, and viability over other optimization problems. [Copyright &y& Elsevier]

Recent research shows that simulated annealing with orthogonal array based neighbourhood functions can help in the search for a solution to a parametrical problem which is closer to an optimum when compared with conventional simulated annealing. Previous studies of simulated annealing analyzed only the main effects of variables of parametrical problems. In fact, both main effects of variables and interactions between variables should be considered, since interactions between variables exist in many parametrical problems. In this paper, an improved orthogonal array based neighbourhood function (IONF) for simulated annealing with the consideration of interaction effects between variables is described. After solving a set of parametrical benchmark function problems where interaction effects between variables exist, results of the benchmark tests show that the proposed simulated annealing algorithm with the IONF outperforms significantly both the simulated annealing algorithms with the existing orthogonal array based neighbourhood functions and the standard neighbourhood functions. Finally, the improved orthogonal array based simulated annealing was applied on the optimization of emulsified dynamite packing-machine design by which the applicability of the algorithm in real world problems can be evaluated and its effectiveness can be further validated. [Copyright &y& Elsevier]

In this paper, a new compact algorithm is proposed. Two sinusoidal formulas are used to automatically adjust the crossover rate and the mutation scaling factor in the compact Differential Evolution (cDE) metaheuristic. The proposed algorithm, called Compound Sinusoidal cDE, CScDE, is compared to seven state-of-the-art compact algorithms on the well-known BBOB test-bed, the CEC-2014 test suite for continuous optimisation, as well as five real-world optimisation problems chosen from the CEC-2011 benchmarks. The CScDE algorithm outperformed its competitors for most problem categories and over most dimensions. It is also compared to some well-established population-based metaheuristics. • A compact compound sinusoidal differential evolution, CScDE, is proposed. • CScDE uses sinusoidal formulas to adjust the parameters of compact DE. • Both binomial and exponential crossover are tested. • CScDE is applied on 89 reference problems with several dimensions. • CScDE outperformed state-of-the-art compact algorithms. [ABSTRACT FROM AUTHOR]

• A two-stage evolution strategy is proposed for solving multi-objective problem. • The convergence and diversity should be balanced in multi-objective optimization. • A local searching method can improve the diversity of solutions in the space. • Experimental results have been presented by using statistical method. The balance of convergence and diversity plays a significant role to the performance of multi-objective evolutionary algorithms (MOEAs). The MOEA/D is a very popular multi-objective optimization algorithm and has been used to solve various real world problems. Like many other algorithms, the MOEA/D also has insufficient ability of convergence and diversity when tackling certain complex multi-objective optimization problems (MOPs). In this paper, a novel algorithm named MOEA/D-TS is proposed for effectively solving MOPs. The new algorithm adopts two stages evolution strategies, the first stage is focused on pushing the solutions into the area of the Pareto front and speeding up its convergence ability, after that, the second stage conducts in the operating solution's diversity and makes the solutions distributed uniformly. The performance of MOEA/D-TS is validated in the ZDT, DTLZ and IMOP problems. Compared with others popular and variants algorithms, the experimental results demonstrate that the proposed algorithm has advantage over other algorithms with regard to the convergence and diversity in most of the tested problems. [ABSTRACT FROM AUTHOR]

• A novel variant of salp swarm algorithm is proposed. • The proposal fruitfully employs three simple but effective methodologies. • It is applied to 35 benchmark test functions and four real-life application problems. • Results are widely compared to the relevant results in literature. • The findings are highly impressive, ratifying the overt potential of this work. This paper propounds a modified version of the salp swarm algorithm (mSSA) for solving optimization problems more prolifically. This technique is refined from the base version with three simple but effective modifications. In the first one, the most important parameter in SSA responsible for balancing exploration and exploitation is chaotically changed by embedding a sinusoidal map in it to catch a better balance between exploration and exploitation from the first iteration until the last. As a short falling, SSA can't exchange information amongst leaders of the chain. Therefore, a mutualistic relationship between two leader salps is included in mSSA to raise its search performance. Additionally, a random technique is systematically applied to the follower salps to introduce diversity in the chain. This can be since there may be some salps in the chain that do not necessarily follow the leader for exploring unvisited areas of the search space. Several test problems are solved by the advocated approach and results are presented in comparison with the relevant results in the available literature. It is ascertained that mSSA, despite its simplicity, significantly outperforms not only the basic SSA but also numerous recent algorithms in terms of fruitful solution precision and convergent trend line. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a discussion of “Solution of economic dispatch problems by seeker optimization algorithm” by Binod Shaw et al. “Expert Systems with Applications”, 39 January (2012) 508–519. The discussed paper presented economic dispatch problem by experimenting with four example systems considering 20, 38, 40 and 10-unit test systems considering convex and nonconvex cost functions with and without transmission loss. However in the reported results, the total cost calculations are different for the given generation schedule. In this discussion, clarification regarding cost calculations is presented. [Copyright &y& Elsevier]