A Metaheuristic Algorithm Based on Chemotherapy Science: CSA

Abstract

Among scientific fields of study, mathematical programming has high status and its importance has led researchers to develop accurate models and effective solving approaches to addressing optimization problems. In particular, metaheuristic algorithms are approximate methods for solving optimization problems whereby good (not necessarily optimum) solutions can be generated via their implementation. In this study, we propose a population-based metaheuristic algorithm according to chemotherapy method to cure cancers that mainly search the infeasible region. As in chemotherapy, Chemotherapy Science Algorithm (CSA) tries to kill inappropriate solutions (cancers and bad cells of the human body); however, this would inevitably risk incidentally destroying some acceptable solutions (healthy cells). In addition, as the cycle of cancer treatment repeats over and over, the algorithm is iterated. To align chemotherapy process with the proposed algorithm, different basic terms and definitions including Infeasibility Function (IF), objective function (OF), Cell Area (CA), and Random Cells (RCs) are presented in this study. In the terminology of algorithms and optimization, IF and OF are mainly applicable as criteria to compare every pair of generated solutions. Finally, we test CSA and its structure using the benchmark Traveling Salesman Problem (TSP).

Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

“…Matrix ̅ , is the gain or the coefficient of the filter impulse response represented in a state-space and can be obtained as in (4).…”

“…The classifications are dependent on the characteristics and focus of study such as memory usage versus memoryless methods, the iterative versus greedy methods, one versus various neighborhood structures, and dynamic versus static objective function (1)(2)(3). Most of existing metaheuristic algorithms in the literature are nature-inspired (4,5), which led Fister et al (6) to present a new view of metaheuristic algorithms' classification based on the inspirational source. Fister et al classified metaheuristic algorithms into four main classifications: swarm intelligence-based, bio-inspired non-swarm intelligence-based, physics or chemistry-based, and others.…”

“…In SI algorithms, each agent performs a series of certain operations and shares their information among others (13). SI algorithms have attracted much attention from the research community, as most of the proposed SI algorithms in the literature are bio-inspired (4,5). The Grey Wolf Optimizer (GWO) (12), Salp Swarm Algorithm (SSA) (14), Grasshopper Optimization Algorithm (GOA) (15), Spotted Hyena Optimizer (SHO) (16), Pity Beetle Algorithm (PBA) (17), and Sailfish Optimizer (18) are among modern SI algorithms.…”

“…From another viewpoint, metaheuristic algorithms are also classified based on the number of agents used in the search strategy, including single-solution (also known as a single-agent) and population-based solution (also known as a multi-agent). The classification based on the number of agents provides a clear view of the algorithms (2) and has motivated some researchers in describing metaheuristic algorithms (4,10,(25)(26)(27)(28). Single-solution and population-based metaheuristic algorithms have both been successfully applied to many optimization problems.…”

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

“…Examples of physical-based metaheuristic are Central force optimization (CFO) [30], Gravitational search algorithm (GSA) [31], big bang-big crunch (BBBC) [32], electromagnetic field optimization (EFO) [33], water evaporation optimization (WEO) [34], Multi-verse optimizer (MVO) [35], and thermal exchange optimization (TEO) [36]. The chemical-based metaheuristic category is inspired by the chemical process.These include Artificial chemical process (ACP) [37], chemical reaction optimization (CRO) [38], artificial chemical reaction optimization (ACRO) [39], gases Brownian motion optimization (GBMO) [40], and chemotherapy science algorithm (CSA) [41]. Human-based metaheuristic algorithms simulate the intelligent behaviour in solving the problems.…”