In this paper, a new grey wolf optimizer (GWO) variant based on a novel weighted distance (WD) called the GWO-WD algorithm is presented to solve global optimization problems. First, a modified position-updating equation formulated using the proposed strategy is employed to obtain additional information and improved global solutions. Then, several of the worst individuals are eliminated and repositioned using an elimination and repositioning strategy to improve the capability of the algorithm and avoid falling into local optima. The performance of the algorithm is verified by utilizing 23 widely used benchmark test functions, the IEEE CEC2014 test suite and three well-known engineering design problems. The simulation results of the proposed algorithm are compared with those of the standard GWO algorithm, three GWO variants and several existing methods, and the proposed algorithm is revealed to be very competitive and, in many cases, superior.

Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes) are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products) would influence such decisions. This paper presents a network‐based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles) besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision‐making.

The economic load dispatch (ELD) problem is a complex optimization problem in power systems. The main task for this optimization problem is to minimize the total fuel cost of generators while also meeting the conditional constraints of valve-point loading effects, prohibited operating zones, and nonsmooth cost functions. In this paper, a novel grey wolf optimization (GWO), abbreviated as NGWO, is proposed to solve the ELD problem by introducing an independent local search strategy and a noninferior solution neighborhood independent local search technique to the original GWO algorithm to achieve the best problem solution. A local search strategy is added to the standard GWO algorithm in the NGWO, which is called GWOI, to search the local neighborhood of the global optimal point in depth and to guarantee a better candidate. In addition, a noninferior solution neighborhood independent local search method is introduced into the GWOI algorithm to find a better solution in the noninferior solution neighborhood and ensure the high probability of jumping out of the local optimum. The feasibility of the proposed NGWO method is verified on five different power systems, and it is compared with other selected methods in terms of the solution quality, convergence rate, and robustness. The compared experimental results indicate that the proposed NGWO method can efficiently solve ELD problems with higher-quality solutions.

The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population‐based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position‐updated equation, which disregards the positional interaction information about the three best grey wolves (i.e., the three leaders). This paper proposes an improved version of the GWO algorithm that is based on a dynamically dimensioned search, spiral walking predation technique, and positional interaction information (referred to as the DGWO). In addition, a nonlinear control parameter strategy, i.e., the control parameter that is nonlinearly increased with an increase in iterations, is designed to balance the exploration and exploitation of the GWO algorithm. The experimental results for 23 general benchmark functions and 3 well‐known engineering optimization design applications validate the effectiveness and feasibility of the proposed DGWO algorithm. The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks. The DGWO provides the highest solution precision, strongest robustness, and fastest convergence rate among the compared algorithms in almost all cases.

The grey wolf optimizer (GWO) algorithm is a recently proposed optimization technique based on the social leadership and hunting behavior of grey wolves in nature. Due to its small number of control parameters, ease of implementation and high level of exploration and exploitation, the GWO has attracted the interest of researchers from different fields. However, the GWO has problems with its position-updated equation, which is good for exploitation but not conducive to exploration because it can prematurely convergence to local optima. To overcome this drawback, a chaotic dynamic weight grey wolf optimizer (CDGWO) is proposed. In the CDGWO algorithm, a new position-updated equation is presented by applying a chaotic map and dynamic weight to guide the search process for potential candidate solutions. In addition, a nonlinear control parameter strategy is designed to balance the exploration and exploitation and accelerate the convergence speed of the GWO algorithm. The search accuracy and performance of the modified position-updated equation and the nonlinear control parameter strategy are verified using 19 well-known classical benchmark functions. The experimental results show that, for almost all benchmark functions, the CDGWO algorithm gives competitive results in terms of convergence, solution quality and local optimal avoidance compared with other nature-inspired optimizations and GWO variants.