The Theory of Incentives I: The Principal-Agent Model

Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

Metaheuristic research: a comprehensive survey

Supply chain coordination with revenue sharing contracts

A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm

Meta-heuristics are problem-independent optimization techniques which provide an optimal solution by exploring and exploiting the entire search space iteratively. These techniques have been successfully engaged to solve distinct real-life and multidisciplinary problems. A good amount of literature has been already published on the design and role of various meta-heuristic algorithms and on their variants. The aim of this study is to present a comprehensive analysis of nature-inspired meta-heuristic utilized in the domain of feature selection. A systematic review methodology has been used for synthesis and analysis of one hundered and seventy six articles. It is one of the important multidisciplinary research areas that assist in finding an optimal set of features so that a better rate of classification can be achieved. The concept of feature selection process along with relevance and redundancy metric is briefly elucidated. A categorical list of nature-inspired meta-heuristic techniques has been presented. The major applications of these techniques are explored to highlight the least and most explored areas. The area of disease diagnosis has been extensively assessed. In addition, the special attention has been given on highlighting the role and performance of binary and chaotic variants of different nature-inspired meta-heuristic techniques. The summary of nature-inspired meta-heuristic methods and their variants along with datasets, performance (mean, best, worst, error rate and standard deviation) is also depicted. In addition, the detailed publication trend of meta-heuristic feature selection approaches has also been presented. The research gaps have been identified for the researcher who inclines to design or analyze the performance of divergent meta-heuristic techniques in solving feature selection problem.

A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem

Given the increased awareness of the need for environmental protection, developing and selling new energy vehicles has gradually become the primary trend in the automobile industry. Collaborating with battery manufacturers to develop new energy vehicles at a lower cost and with better performance is key for automobile manufacturers to maintain their dominant position. We examine a research and development (R&D) collaboration alliance, consisting of an R&D phase and a sales phase, between an automobile manufacturer (the marketer) and a battery manufacturer (the innovator) to develop and bring an innovative green technology to market. This paper investigates four types of R&D collaboration contracts with different payments: vertical R&D collaboration contracts with either a revenue-sharing payment (Case VR) or a fixed payment (Case VF) and co-development contracts in which either the marketer operates the sales phase (Case CM) or the innovator operates the sales phase (Case CI). Our analysis reveals that no contract is always the best choice from the marketer’s perspective. Specifically, the contract preferred by the marketer depends on the trade-off between the R&D efficiency and the sales efficiency of the marketer and the innovator, respectively. Interestingly, becoming involved in the R&D phase is not always in the marketer’s best interest, i.e., the marketer does not always prefer the co-development contract. Counterintuitively, we find that the marketer does not always choose to sell the green product, even when its marketing efficiency is high. Furthermore, under certain circumstances, it is more advantageous for the marketer to propose that the innovator sell the green product rather than doing so itself.

Strategic green technology innovation in a two-stage alliance: Vertical collaboration or co-development?

Asymmetric retailers with different moving sequences: Group buying vs. individual purchasing

Modeling fuzzy multi-period production planning and sourcing problem with credibility service levels

Differential evolution (DE) is a remarkable evolutionary algorithm for global optimization over continuous search space, whose performance is significantly influenced by its mutation operator and control parameters (scaling factor and crossover rate). In order to enhance the performance of DE, we adopt a novel Gaussian mutation operator and a modified common mutation operator to collaboratively produce new mutant vectors, and employ a periodic function and a Gaussian function to generate the required values of scaling factor and crossover rate, respectively. In the proposed variant of DE (denoted by GPDE), the two adopted mutation operators are adaptively applied to generate the corresponding mutant vector of each individual based on their own cumulative scores, the periodic scaling factor can provide a better balance between exploration ability and exploitation ability, and the Gaussian function-based crossover rate will possess fluctuant value, which possibly enhance the population diversity. To verify the performance of proposed GPDE, a suite of thirty benchmark functions and four real-world problems are applied to conduct the simulation experiment. The simulation results demonstrate that the proposed GPDE performs significantly better than five state-of-the-art DE variants and other two meta-heuristics algorithms.

Differential evolution with Gaussian mutation and dynamic parameter adjustment

Graphical abstractDisplay Omitted HighlightsWe propose a novel meta-heuristic algorithm called joint operations algorithm.Joint operations algorithm contains offensive, defensive and regroup operations.We compare JOA with six algorithms on 20 functions and four real-life problems.The experimental results show that JOA has the best overall performance. Large-scale global optimization (LSGO) is a very important but thorny task in optimization domain, which widely exists in management and engineering problems. In order to strengthen the effectiveness of meta-heuristic algorithms when handling LSGO problems, we propose a novel meta-heuristic algorithm, which is inspired by the joint operations strategy of multiple military units and called joint operations algorithm (JOA). The overall framework of the proposed algorithm involves three main operations: offensive, defensive and regroup operations. In JOA, offensive operations and defensive operations are used to balance the exploration ability and exploitation ability, and regroup operations is applied to alleviate the problem of premature convergence. To evaluate the performance of the proposed algorithm, we compare JOA with six excellent meta-heuristic algorithms on twenty LSGO benchmark functions of IEEE CEC 2010 special session and four real-life problems. The experimental results show that JOA performs steadily, and it has the best overall performance among the seven compared algorithms.

Yanfei Lan

Papers

Strategic green technology innovation in a two-stage alliance: Vertical collaboration or co-development?

Asymmetric retailers with different moving sequences: Group buying vs. individual purchasing

Modeling fuzzy multi-period production planning and sourcing problem with credibility service levels

Differential evolution with Gaussian mutation and dynamic parameter adjustment

Joint operations algorithm for large-scale global optimization